diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 94c141e4..694d5d99 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -29,7 +29,7 @@ jobs:
           POETRY_PYTHON: ${{ steps.setup-python.outputs.python-path }}
         run: |
           poetry env use $POETRY_PYTHON
-          poetry install --no-interaction --without dev,test
+          poetry install --no-interaction --without dev,test --no-cache
       - name: Build KWJA
         run: |
           poetry build
diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
index 3ecab1ac..a6d83904 100644
--- a/.github/workflows/publish.yml
+++ b/.github/workflows/publish.yml
@@ -18,7 +18,7 @@ jobs:
       - name: Add path for Python packages
         run: echo "$HOME/.local/bin" >> $GITHUB_PATH
       - name: Install dependencies
-        run: poetry install --no-interaction --only main
+        run: poetry install --no-interaction --only main --no-cache
       - name: Build package
         run: poetry build
 
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index 741c930c..24a73668 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -14,7 +14,7 @@ jobs:
         uses: actions/checkout@v4
       - name: Create Release
         id: create_release
-        uses: softprops/action-gh-release@v1
+        uses: softprops/action-gh-release@v2
         if: startsWith(github.ref, 'refs/tags/')
         with:
           body: |
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 13aafcd1..c73095f5 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -24,15 +24,12 @@ jobs:
             echo "$HOME/.local/bin" >> $GITHUB_PATH
       - name: Install dependencies
         run: |
-          poetry config virtualenvs.create false
-          poetry install --no-interaction --without dev
+          poetry install --no-interaction --without dev --no-cache
       - name: Run tests
         run: |
           poetry run pytest --cov=./ --cov-report=xml
-        env:
-          XDG_CACHE_HOME: ${{ github.workspace }}/.cache
       - name: Upload coverage to Codecov
-        uses: codecov/codecov-action@v3
+        uses: codecov/codecov-action@v4
         with:
           token: ${{ secrets.CODECOV_TOKEN }}
           files: ./coverage.xml
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 829e3d94..7d0b5dbb 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -2,42 +2,42 @@ default_language_version:
   python: python3.10
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.5.0
+    rev: v4.6.0
     hooks:
       - id: end-of-file-fixer
       - id: trailing-whitespace
       - id: check-yaml
       - id: check-toml
   - repo: https://github.com/psf/black-pre-commit-mirror
-    rev: 23.11.0
+    rev: 24.4.0
     hooks:
       - id: black
   - repo: https://github.com/PyCQA/flake8
-    rev: 6.1.0
+    rev: 7.0.0
     hooks:
       - id: flake8
         additional_dependencies: [Flake8-pyproject]
   - repo: https://github.com/PyCQA/isort
-    rev: 5.12.0
+    rev: 5.13.2
     hooks:
       - id: isort
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.7.0
+    rev: v1.9.0
     hooks:
       - id: mypy
         additional_dependencies:
           - rhoknp==1.6.0
           - hydra-core==1.3.2
-          - torch==2.1.1
-          - torchmetrics==1.2.0
-          - transformers==4.34.1
+          - torch==2.2.0
+          - torchmetrics==1.3.0
+          - transformers==4.38.2
           - tokenizers
           - wandb
           - typer
           - types-PyYAML
           - cohesion-tools==0.5.7
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.15.0
+    rev: v3.15.2
     hooks:
       - id: pyupgrade
         args:
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 239f4bb5..b948606c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -7,6 +7,17 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [v2.4.0] - 2024-07-18
+### Added
+- Introduce a special token to handle whitespaces as they are.
+
+### Changed
+- Set default value of num_beams to 1.
+- Refactor seq2seq module to make it slightly faster.
+
+### Removed
+- Remove normalization of whitespaces to "␣".
+
 ## [v2.3.0] - 2024-02-01
 ### Added
 - Support Python 3.12.
diff --git a/configs/char_module.debug.yaml b/configs/char_module.debug.yaml
index 1408e082..6f46ccad 100644
--- a/configs/char_module.debug.yaml
+++ b/configs/char_module.debug.yaml
@@ -41,6 +41,7 @@ compile: ${oc.env:COMPILE,false}
 ignore_hparams_on_save: false
 
 # constants
+special_tokens: [" "]
 hparams_to_ignore_on_save:
   - project
   - work_dir
diff --git a/configs/char_module.yaml b/configs/char_module.yaml
index 4169d165..9a029e4a 100644
--- a/configs/char_module.yaml
+++ b/configs/char_module.yaml
@@ -41,6 +41,7 @@ compile: ${oc.env:COMPILE,false}
 ignore_hparams_on_save: false
 
 # constants
+special_tokens: [" "]
 hparams_to_ignore_on_save:
   - project
   - work_dir
diff --git a/configs/datamodule/base/char.yaml b/configs/datamodule/base/char.yaml
index f10e643e..77865271 100644
--- a/configs/datamodule/base/char.yaml
+++ b/configs/datamodule/base/char.yaml
@@ -4,5 +4,6 @@ denormalize_probability: ${denormalize_probability}
 tokenizer:
   _target_: transformers.AutoTokenizer.from_pretrained
   pretrained_model_name_or_path: ${encoder.pretrained_model_name_or_path}
+  additional_special_tokens: ${special_tokens}
   do_word_tokenize: false
   _convert_: all
diff --git a/configs/datamodule/predict/char_inference.yaml b/configs/datamodule/predict/char_inference.yaml
index c22ebe5c..a16a6aaf 100644
--- a/configs/datamodule/predict/char_inference.yaml
+++ b/configs/datamodule/predict/char_inference.yaml
@@ -3,5 +3,7 @@ defaults:
   - _self_
 
 _target_: kwja.datamodule.datasets.CharInferenceDataset
-texts: []
 doc_id_prefix: null
+# texts and raw_text_file are mutually exclusive
+texts: []
+raw_text_file: null
diff --git a/configs/datamodule/predict/seq2seq_inference.yaml b/configs/datamodule/predict/seq2seq_inference.yaml
index 6ff6d232..1f8d9eb8 100644
--- a/configs/datamodule/predict/seq2seq_inference.yaml
+++ b/configs/datamodule/predict/seq2seq_inference.yaml
@@ -3,5 +3,4 @@ defaults:
   - _self_
 
 _target_: kwja.datamodule.datasets.Seq2SeqInferenceDataset
-texts: []
-doc_id_prefix: null
+juman_file: null
diff --git a/configs/datamodule/predict/typo_inference.yaml b/configs/datamodule/predict/typo_inference.yaml
index 2a11caf6..8d2ac17a 100644
--- a/configs/datamodule/predict/typo_inference.yaml
+++ b/configs/datamodule/predict/typo_inference.yaml
@@ -3,4 +3,6 @@ defaults:
   - _self_
 
 _target_: kwja.datamodule.datasets.TypoInferenceDataset
+# texts and raw_text_file are mutually exclusive
 texts: []
+raw_text_file: null
diff --git a/configs/eval.yaml b/configs/eval.yaml
index ef3de213..a4d1a95d 100644
--- a/configs/eval.yaml
+++ b/configs/eval.yaml
@@ -25,7 +25,7 @@ dependency_topk: 4
 discourse_threshold: 0.0
 
 # environment dependent settings
-num_workers: -1
+num_workers: ${oc.env:NUM_WORKERS,0}
 devices: ${oc.env:DEVICES,1}
-max_batches_per_device: 4
-compile: false
+max_batches_per_device: ${oc.env:MAX_BATCHES_PER_DEVICE,2}
+compile: ${oc.env:COMPILE,false}
diff --git a/configs/seq2seq_module.debug.yaml b/configs/seq2seq_module.debug.yaml
index 665a0513..7e6ae5d0 100644
--- a/configs/seq2seq_module.debug.yaml
+++ b/configs/seq2seq_module.debug.yaml
@@ -16,10 +16,10 @@ do_predict_after_train: false
 checkpoint_path: ""
 
 # For decoding settings
-use_forced_decoding: true
+use_surf_forced_decoding: true
 decoding:
   max_length: ${max_tgt_length}
-  num_beams: 3
+  num_beams: 1
 
 # set monitor and mode for early_stopping and model_checkpoint
 monitor: valid/seq2seq_loss
diff --git a/configs/seq2seq_module.yaml b/configs/seq2seq_module.yaml
index 4d4687ac..d8f797a7 100644
--- a/configs/seq2seq_module.yaml
+++ b/configs/seq2seq_module.yaml
@@ -16,10 +16,10 @@ do_predict_after_train: false
 checkpoint_path: ""
 
 # For decoding settings
-use_forced_decoding: true
+use_surf_forced_decoding: true
 decoding:
   max_length: ${max_tgt_length}
-  num_beams: 3
+  num_beams: 1
 
 # set monitor and mode for early_stopping and model_checkpoint
 monitor: valid/seq2seq_loss
diff --git a/configs/typo_module.debug.yaml b/configs/typo_module.debug.yaml
index 4931578a..38d89623 100644
--- a/configs/typo_module.debug.yaml
+++ b/configs/typo_module.debug.yaml
@@ -35,7 +35,7 @@ compile: ${oc.env:COMPILE,false}
 ignore_hparams_on_save: false
 
 # constants
-special_tokens: ["<k>", "<d>", "<_>", "<dummy>"]
+special_tokens: ["<k>", "<d>", "<_>", "<dummy>", " "]
 hparams_to_ignore_on_save:
   - project
   - work_dir
diff --git a/configs/typo_module.yaml b/configs/typo_module.yaml
index 65ec8090..115ae76f 100644
--- a/configs/typo_module.yaml
+++ b/configs/typo_module.yaml
@@ -36,7 +36,7 @@ compile: ${oc.env:COMPILE,false}
 ignore_hparams_on_save: false
 
 # constants
-special_tokens: ["<k>", "<d>", "<_>", "<dummy>"]
+special_tokens: ["<k>", "<d>", "<_>", "<dummy>", " "]
 hparams_to_ignore_on_save:
   - project
   - work_dir
diff --git a/configs/word_module.debug.yaml b/configs/word_module.debug.yaml
index 06891307..1419a6fa 100644
--- a/configs/word_module.debug.yaml
+++ b/configs/word_module.debug.yaml
@@ -66,7 +66,7 @@ compile: ${oc.env:COMPILE,false}
 ignore_hparams_on_save: false
 
 # constants
-special_tokens: ["[著者]", "[読者]", "[不特定:人]", "[不特定:物]", "[NULL]", "[NA]", "[ROOT]"]
+special_tokens: ["[著者]", "[読者]", "[不特定:人]", "[不特定:物]", "[NULL]", "[NA]", "[ROOT]", " "]
 hparams_to_ignore_on_save:
   - project
   - work_dir
diff --git a/configs/word_module.yaml b/configs/word_module.yaml
index 135219fd..80dca19f 100644
--- a/configs/word_module.yaml
+++ b/configs/word_module.yaml
@@ -66,7 +66,7 @@ compile: ${oc.env:COMPILE,false}
 ignore_hparams_on_save: false
 
 # constants
-special_tokens: ["[著者]", "[読者]", "[不特定:人]", "[不特定:物]", "[NULL]", "[NA]", "[ROOT]"]
+special_tokens: ["[著者]", "[読者]", "[不特定:人]", "[不特定:物]", "[NULL]", "[NA]", "[ROOT]", " "]
 hparams_to_ignore_on_save:
   - project
   - work_dir
diff --git a/poetry.lock b/poetry.lock
index 711f7ed4..5246e56c 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,88 +1,88 @@
-# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "aiohttp"
-version = "3.9.3"
+version = "3.9.4"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -479,18 +479,18 @@ tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipyth
 
 [[package]]
 name = "filelock"
-version = "3.13.1"
+version = "3.13.4"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "filelock-3.13.1-py3-none-any.whl", hash = "sha256:57dbda9b35157b05fb3e58ee91448612eb674172fab98ee235ccb0b5bee19a1c"},
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 ]
 
 [package.extras]
-docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.24)"]
-testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"]
+docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"]
+testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8.0.1)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"]
 typing = ["typing-extensions (>=4.8)"]
 
 [[package]]
@@ -581,18 +581,17 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2023.12.2"
+version = "2024.3.1"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""}
-requests = {version = "*", optional = true, markers = "extra == \"http\""}
 
 [package.extras]
 abfs = ["adlfs"]
@@ -609,7 +608,7 @@ github = ["requests"]
 gs = ["gcsfs"]
 gui = ["panel"]
 hdfs = ["pyarrow (>=1)"]
-http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "requests"]
+http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
 libarchive = ["libarchive-c"]
 oci = ["ocifs"]
 s3 = ["s3fs"]
@@ -634,20 +633,21 @@ smmap = ">=3.0.1,<6"
 
 [[package]]
 name = "gitpython"
-version = "3.1.41"
+version = "3.1.43"
 description = "GitPython is a Python library used to interact with Git repositories"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "GitPython-3.1.43.tar.gz", hash = "sha256:35f314a9f878467f5453cc1fee295c3e18e52f1b99f10f6cf5b1682e968a9e7c"},
 ]
 
 [package.dependencies]
 gitdb = ">=4.0.1,<5"
 
 [package.extras]
-test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "sumtypes"]
+doc = ["sphinx (==4.3.2)", "sphinx-autodoc-typehints", "sphinx-rtd-theme", "sphinxcontrib-applehelp (>=1.0.2,<=1.0.4)", "sphinxcontrib-devhelp (==1.0.2)", "sphinxcontrib-htmlhelp (>=2.0.0,<=2.0.1)", "sphinxcontrib-qthelp (==1.0.3)", "sphinxcontrib-serializinghtml (==1.1.5)"]
+test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"]
 
 [[package]]
 name = "gprof2dot"
@@ -662,13 +662,13 @@ files = [
 
 [[package]]
 name = "huggingface-hub"
-version = "0.20.3"
+version = "0.22.2"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "huggingface_hub-0.20.3-py3-none-any.whl", hash = "sha256:d988ae4f00d3e307b0c80c6a05ca6dbb7edba8bba3079f74cda7d9c2e562a7b6"},
-    {file = "huggingface_hub-0.20.3.tar.gz", hash = "sha256:94e7f8e074475fbc67d6a71957b678e1b4a74ff1b64a644fd6cbb83da962d05d"},
+    {file = "huggingface_hub-0.22.2-py3-none-any.whl", hash = "sha256:3429e25f38ccb834d310804a3b711e7e4953db5a9e420cc147a5e194ca90fd17"},
+    {file = "huggingface_hub-0.22.2.tar.gz", hash = "sha256:32e9a9a6843c92f253ff9ca16b9985def4d80a93fb357af5353f770ef74a81be"},
 ]
 
 [package.dependencies]
@@ -681,15 +681,17 @@ tqdm = ">=4.42.1"
 typing-extensions = ">=3.7.4.3"
 
 [package.extras]
-all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.1.3)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.3.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 cli = ["InquirerPy (==0.3.4)"]
-dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.1.3)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.3.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
-inference = ["aiohttp", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)"]
-quality = ["mypy (==1.5.1)", "ruff (>=0.1.3)"]
+hf-transfer = ["hf-transfer (>=0.1.4)"]
+inference = ["aiohttp", "minijinja (>=1.0)"]
+quality = ["mypy (==1.5.1)", "ruff (>=0.3.0)"]
 tensorflow = ["graphviz", "pydot", "tensorflow"]
-testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
-torch = ["torch"]
+tensorflow-testing = ["keras (<3.0)", "tensorflow"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "minijinja (>=1.0)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
+torch = ["safetensors", "torch"]
 typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
 
 [[package]]
@@ -711,24 +713,24 @@ packaging = "*"
 
 [[package]]
 name = "idna"
-version = "3.6"
+version = "3.7"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
 python-versions = ">=3.5"
 files = [
-    {file = "idna-3.6-py3-none-any.whl", hash = "sha256:c05567e9c24a6b9faaa835c4821bad0590fbb9d5779e7caa6e1cc4978e7eb24f"},
-    {file = "idna-3.6.tar.gz", hash = "sha256:9ecdbbd083b06798ae1e86adcbfe8ab1479cf864e4ee30fe4e46a003d12491ca"},
+    {file = "idna-3.7-py3-none-any.whl", hash = "sha256:82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0"},
+    {file = "idna-3.7.tar.gz", hash = "sha256:028ff3aadf0609c1fd278d8ea3089299412a7a8b9bd005dd08b9f8285bcb5cfc"},
 ]
 
 [[package]]
 name = "importlib-resources"
-version = "6.1.1"
+version = "6.4.0"
 description = "Read resources from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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@@ -736,7 +738,7 @@ zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
 
 [package.extras]
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+testing = ["jaraco.test (>=5.4)", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)", "zipp (>=3.17)"]
 
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 name = "iniconfig"
@@ -749,6 +751,20 @@ files = [
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 ]
 
+[[package]]
+name = "intel-openmp"
+version = "2021.4.0"
+description = "Intel OpenMP* Runtime Library"
+optional = false
+python-versions = "*"
+files = [
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 version = "0.13.13"
@@ -886,13 +902,13 @@ pyknp = ["pyknp[pyknp] (>=0.6,<1.0)"]
 
 [[package]]
 name = "jinja2"
-version = "3.1.3"
+version = "3.1.4"
 description = "A very fast and expressive template engine."
 optional = false
 python-versions = ">=3.7"
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+version = "1.4.0"
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 version = "1.3.0"
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 ]
 
 [[package]]
@@ -1464,23 +1514,24 @@ nvidia-nvjitlink-cu12 = "*"
 
 [[package]]
 name = "nvidia-nccl-cu12"
-version = "2.19.3"
+version = "2.20.5"
 description = "NVIDIA Collective Communication Library (NCCL) Runtime"
 optional = false
 python-versions = ">=3"
 files = [
-    {file = "nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl", hash = "sha256:a9734707a2c96443331c1e48c717024aa6678a0e2a4cb66b2c364d18cee6b48d"},
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+    {file = "nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl", hash = "sha256:057f6bf9685f75215d0c53bf3ac4a10b3e6578351de307abad9e18a99182af56"},
 ]
 
 [[package]]
 name = "nvidia-nvjitlink-cu12"
-version = "12.3.101"
+version = "12.4.127"
 description = "Nvidia JIT LTO Library"
 optional = false
 python-versions = ">=3"
 files = [
-    {file = "nvidia_nvjitlink_cu12-12.3.101-py3-none-manylinux1_x86_64.whl", hash = "sha256:64335a8088e2b9d196ae8665430bc6a2b7e6ef2eb877a9c735c804bd4ff6467c"},
-    {file = "nvidia_nvjitlink_cu12-12.3.101-py3-none-win_amd64.whl", hash = "sha256:1b2e317e437433753530792f13eece58f0aec21a2b05903be7bffe58a606cbd1"},
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 ]
 
 [[package]]
@@ -1511,13 +1562,13 @@ PyYAML = ">=5.1.0"
 
 [[package]]
 name = "packaging"
-version = "23.2"
+version = "24.0"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [[package]]
@@ -1557,8 +1608,8 @@ files = [
 [package.dependencies]
 numpy = [
     {version = ">=1.20.3", markers = "python_version < \"3.10\""},
-    {version = ">=1.21.0", markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
     {version = ">=1.23.2", markers = "python_version >= \"3.11\""},
+    {version = ">=1.21.0", markers = "python_version >= \"3.10\" and python_version < \"3.11\""},
 ]
 python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
@@ -1589,18 +1640,18 @@ xml = ["lxml (>=4.6.3)"]
 
 [[package]]
 name = "parso"
-version = "0.8.3"
+version = "0.8.4"
 description = "A Python Parser"
 optional = false
 python-versions = ">=3.6"
 files = [
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 ]
 
 [package.extras]
-qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
-testing = ["docopt", "pytest (<6.0.0)"]
+qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
+testing = ["docopt", "pytest"]
 
 [[package]]
 name = "pexpect"
@@ -1629,13 +1680,13 @@ files = [
 
 [[package]]
 name = "pluggy"
-version = "1.4.0"
+version = "1.5.0"
 description = "plugin and hook calling mechanisms for python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -1658,22 +1709,22 @@ wcwidth = "*"
 
 [[package]]
 name = "protobuf"
-version = "4.25.2"
+version = "4.25.3"
 description = ""
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "protobuf-4.25.3-cp38-cp38-win_amd64.whl", hash = "sha256:c053062984e61144385022e53678fbded7aea14ebb3e0305ae3592fb219ccfa4"},
+    {file = "protobuf-4.25.3-cp39-cp39-win32.whl", hash = "sha256:19b270aeaa0099f16d3ca02628546b8baefe2955bbe23224aaf856134eccf1e4"},
+    {file = "protobuf-4.25.3-cp39-cp39-win_amd64.whl", hash = "sha256:e3c97a1555fd6388f857770ff8b9703083de6bf1f9274a002a332d65fbb56c8c"},
+    {file = "protobuf-4.25.3-py3-none-any.whl", hash = "sha256:f0700d54bcf45424477e46a9f0944155b46fb0639d69728739c0e47bab83f2b9"},
+    {file = "protobuf-4.25.3.tar.gz", hash = "sha256:25b5d0b42fd000320bd7830b349e3b696435f3b329810427a6bcce6a5492cc5c"},
 ]
 
 [[package]]
@@ -1762,13 +1813,13 @@ windows-terminal = ["colorama (>=0.4.6)"]
 
 [[package]]
 name = "pytest"
-version = "8.0.0"
+version = "8.2.2"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pytest-8.0.0-py3-none-any.whl", hash = "sha256:50fb9cbe836c3f20f0dfa99c565201fb75dc54c8d76373cd1bde06b06657bdb6"},
-    {file = "pytest-8.0.0.tar.gz", hash = "sha256:249b1b0864530ba251b7438274c4d251c58d868edaaec8762893ad4a0d71c36c"},
+    {file = "pytest-8.2.2-py3-none-any.whl", hash = "sha256:c434598117762e2bd304e526244f67bf66bbd7b5d6cf22138be51ff661980343"},
+    {file = "pytest-8.2.2.tar.gz", hash = "sha256:de4bb8104e201939ccdc688b27a89a7be2079b22e2bd2b07f806b6ba71117977"},
 ]
 
 [package.dependencies]
@@ -1776,11 +1827,11 @@ colorama = {version = "*", markers = "sys_platform == \"win32\""}
 exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""}
 iniconfig = "*"
 packaging = "*"
-pluggy = ">=1.3.0,<2.0"
-tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
+pluggy = ">=1.5,<2.0"
+tomli = {version = ">=1", markers = "python_version < \"3.11\""}
 
 [package.extras]
-testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
+dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
 [[package]]
 name = "pytest-cov"
@@ -1821,13 +1872,13 @@ tests = ["pytest-virtualenv"]
 
 [[package]]
 name = "python-dateutil"
-version = "2.8.2"
+version = "2.9.0.post0"
 description = "Extensions to the standard Python datetime module"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
 files = [
-    {file = "python-dateutil-2.8.2.tar.gz", hash = "sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86"},
-    {file = "python_dateutil-2.8.2-py2.py3-none-any.whl", hash = "sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9"},
+    {file = "python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3"},
+    {file = "python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"},
 ]
 
 [package.dependencies]
@@ -1849,13 +1900,13 @@ cli = ["click (>=5.0)"]
 
 [[package]]
 name = "pytorch-lightning"
-version = "2.1.3"
+version = "2.1.4"
 description = "PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pytorch-lightning-2.1.3.tar.gz", hash = "sha256:2500b002fa09cb37b0e12f879876bf30a2d260b0f04783d33264dab175f0c966"},
-    {file = "pytorch_lightning-2.1.3-py3-none-any.whl", hash = "sha256:03ed186035a230b161130e0d8ecf1dd6657ff7e3f1520e9257b0db7650f9aeea"},
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 name = "scipy"
@@ -2470,109 +2525,117 @@ test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeo
 
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 [[package]]
 name = "sentry-sdk"
-version = "1.40.0"
+version = "1.45.0"
 description = "Python client for Sentry (https://sentry.io)"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
@@ -2586,6 +2649,7 @@ asyncpg = ["asyncpg (>=0.23)"]
 beam = ["apache-beam (>=2.12)"]
 bottle = ["bottle (>=0.12.13)"]
 celery = ["celery (>=3)"]
+celery-redbeat = ["celery-redbeat (>=2)"]
 chalice = ["chalice (>=1.16.0)"]
 clickhouse-driver = ["clickhouse-driver (>=0.2.0)"]
 django = ["django (>=1.8)"]
@@ -2596,6 +2660,7 @@ grpcio = ["grpcio (>=1.21.1)"]
 httpx = ["httpx (>=0.16.0)"]
 huey = ["huey (>=2)"]
 loguru = ["loguru (>=0.5)"]
+openai = ["openai (>=1.0.0)", "tiktoken (>=0.3.0)"]
 opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
 opentelemetry-experimental = ["opentelemetry-distro (>=0.40b0,<1.0)", "opentelemetry-instrumentation-aiohttp-client (>=0.40b0,<1.0)", "opentelemetry-instrumentation-django (>=0.40b0,<1.0)", "opentelemetry-instrumentation-fastapi (>=0.40b0,<1.0)", "opentelemetry-instrumentation-flask (>=0.40b0,<1.0)", "opentelemetry-instrumentation-requests (>=0.40b0,<1.0)", "opentelemetry-instrumentation-sqlite3 (>=0.40b0,<1.0)", "opentelemetry-instrumentation-urllib (>=0.40b0,<1.0)"]
 pure-eval = ["asttokens", "executing", "pure-eval"]
@@ -2725,19 +2790,29 @@ test = ["pytest"]
 
 [[package]]
 name = "setuptools"
-version = "69.0.3"
+version = "70.3.0"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
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+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
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+
+[[package]]
+name = "shellingham"
+version = "1.5.4"
+description = "Tool to Detect Surrounding Shell"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
 
 [[package]]
 name = "six"
@@ -2794,134 +2869,147 @@ files = [
 [package.dependencies]
 mpmath = ">=0.19"
 
+[[package]]
+name = "tbb"
+version = "2021.13.0"
+description = "Intel® oneAPI Threading Building Blocks (oneTBB)"
+optional = false
+python-versions = "*"
+files = [
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+]
+
 [[package]]
 name = "threadpoolctl"
-version = "3.2.0"
+version = "3.4.0"
 description = "threadpoolctl"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "threadpoolctl-3.4.0.tar.gz", hash = "sha256:f11b491a03661d6dd7ef692dd422ab34185d982466c49c8f98c8f716b5c93196"},
 ]
 
 [[package]]
 name = "tokenizers"
-version = "0.15.1"
+version = "0.15.2"
 description = ""
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
 filelock = "*"
 fsspec = "*"
 jinja2 = "*"
+mkl = {version = ">=2021.1.1,<=2021.4.0", markers = "platform_system == \"Windows\""}
 networkx = "*"
 nvidia-cublas-cu12 = {version = "12.1.3.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-cuda-cupti-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
@@ -2991,10 +3075,10 @@ nvidia-cufft-cu12 = {version = "11.0.2.54", markers = "platform_system == \"Linu
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-nvidia-nccl-cu12 = {version = "2.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nccl-cu12 = {version = "2.20.5", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 sympy = "*"
-triton = {version = "2.2.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+triton = {version = "2.3.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.12\""}
 typing-extensions = ">=4.8.0"
 
 [package.extras]
@@ -3003,13 +3087,13 @@ optree = ["optree (>=0.9.1)"]
 
 [[package]]
 name = "torchmetrics"
-version = "1.3.0.post0"
+version = "1.4.0.post0"
 description = "PyTorch native Metrics"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -3020,25 +3104,26 @@ torch = ">=1.10.0"
 typing-extensions = {version = "*", markers = "python_version < \"3.9\""}
 
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 visual = ["SciencePlots (>=2.0.0)", "matplotlib (>=3.3.0)"]
 
 [[package]]
 name = "tqdm"
-version = "4.66.1"
+version = "4.66.4"
 description = "Fast, Extensible Progress Meter"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "tqdm-4.66.1-py3-none-any.whl", hash = "sha256:d302b3c5b53d47bce91fea46679d9c3c6508cf6332229aa1e7d8653723793386"},
-    {file = "tqdm-4.66.1.tar.gz", hash = "sha256:d88e651f9db8d8551a62556d3cff9e3034274ca5d66e93197cf2490e2dcb69c7"},
+    {file = "tqdm-4.66.4-py3-none-any.whl", hash = "sha256:b75ca56b413b030bc3f00af51fd2c1a1a5eac6a0c1cca83cbb37a5c52abce644"},
+    {file = "tqdm-4.66.4.tar.gz", hash = "sha256:e4d936c9de8727928f3be6079590e97d9abfe8d39a590be678eb5919ffc186bb"},
 ]
 
 [package.dependencies]
@@ -3052,28 +3137,28 @@ telegram = ["requests"]
 
 [[package]]
 name = "traitlets"
-version = "5.14.1"
+version = "5.14.2"
 description = "Traitlets Python configuration system"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "traitlets-5.14.1-py3-none-any.whl", hash = "sha256:2e5a030e6eff91737c643231bfcf04a65b0132078dad75e4936700b213652e74"},
-    {file = "traitlets-5.14.1.tar.gz", hash = "sha256:8585105b371a04b8316a43d5ce29c098575c2e477850b62b848b964f1444527e"},
+    {file = "traitlets-5.14.2-py3-none-any.whl", hash = "sha256:fcdf85684a772ddeba87db2f398ce00b40ff550d1528c03c14dbf6a02003cd80"},
+    {file = "traitlets-5.14.2.tar.gz", hash = "sha256:8cdd83c040dab7d1dee822678e5f5d100b514f7b72b01615b26fc5718916fdf9"},
 ]
 
 [package.extras]
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
-test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<7.5)", "pytest-mock", "pytest-mypy-testing"]
+test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.1)", "pytest-mock", "pytest-mypy-testing"]
 
 [[package]]
 name = "transformers"
-version = "4.36.2"
+version = "4.38.2"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 optional = false
 python-versions = ">=3.8.0"
 files = [
-    {file = "transformers-4.36.2-py3-none-any.whl", hash = "sha256:462066c4f74ee52516f12890dcc9ec71d1a5e97998db621668455117a54330f6"},
-    {file = "transformers-4.36.2.tar.gz", hash = "sha256:d8068e897e47793281501e547d2bbdfc5b8556409c2cb6c3d9e2ca77d4c0b4ec"},
+    {file = "transformers-4.38.2-py3-none-any.whl", hash = "sha256:c4029cb9f01b3dd335e52f364c52d2b37c65b4c78e02e6a08b1919c5c928573e"},
+    {file = "transformers-4.38.2.tar.gz", hash = "sha256:c5fc7ad682b8a50a48b2a4c05d4ea2de5567adb1bdd00053619dbe5960857dd5"},
 ]
 
 [package.dependencies]
@@ -3085,23 +3170,23 @@ protobuf = {version = "*", optional = true, markers = "extra == \"sentencepiece\
 pyyaml = ">=5.1"
 regex = "!=2019.12.17"
 requests = "*"
-safetensors = ">=0.3.1"
+safetensors = ">=0.4.1"
 sentencepiece = {version = ">=0.1.91,<0.1.92 || >0.1.92", optional = true, markers = "extra == \"sentencepiece\""}
 tokenizers = ">=0.14,<0.19"
 tqdm = ">=4.27"
 
 [package.extras]
 accelerate = ["accelerate (>=0.21.0)"]
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+agents = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch"]
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 audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
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 docs-specific = ["hf-doc-builder"]
 flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)"]
 flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
@@ -3109,7 +3194,7 @@ ftfy = ["ftfy"]
 integrations = ["optuna", "ray[tune] (>=2.7.0)", "sigopt"]
 ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
 modelcreation = ["cookiecutter (==1.7.3)"]
-natten = ["natten (>=0.14.6)"]
+natten = ["natten (>=0.14.6,<0.15.0)"]
 onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
 onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
 optuna = ["optuna"]
@@ -3118,36 +3203,36 @@ ray = ["ray[tune] (>=2.7.0)"]
 retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
 sagemaker = ["sagemaker (>=2.31.0)"]
 sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
-serving = ["fastapi", "pydantic (<2)", "starlette", "uvicorn"]
+serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
 sigopt = ["sigopt"]
 sklearn = ["scikit-learn"]
 speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pydantic (<2)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "tensorboard", "timeout-decorator"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "tensorboard", "timeout-decorator"]
 tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
 tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
 tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 timm = ["timm"]
 tokenizers = ["tokenizers (>=0.14,<0.19)"]
-torch = ["accelerate (>=0.21.0)", "torch (>=1.10,!=1.12.0)"]
+torch = ["accelerate (>=0.21.0)", "torch"]
 torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
 torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
-torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.14,<0.19)", "torch (>=1.10,!=1.12.0)", "tqdm (>=4.27)"]
+torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.14,<0.19)", "torch", "tqdm (>=4.27)"]
 video = ["av (==9.2.0)", "decord (==0.6.0)"]
 vision = ["Pillow (>=10.0.1,<=15.0)"]
 
 [[package]]
 name = "triton"
-version = "2.2.0"
+version = "2.3.1"
 description = "A language and compiler for custom Deep Learning operations"
 optional = false
 python-versions = "*"
 files = [
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+    {file = "triton-2.3.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:eaf80e8761a9e3498aa92e7bf83a085b31959c61f5e8ac14eedd018df6fccd10"},
+    {file = "triton-2.3.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b13bf35a2b659af7159bf78e92798dc62d877aa991de723937329e2d382f1991"},
+    {file = "triton-2.3.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:63381e35ded3304704ea867ffde3b7cfc42c16a55b3062d41e017ef510433d66"},
+    {file = "triton-2.3.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1d968264523c7a07911c8fb51b4e0d1b920204dae71491b1fe7b01b62a31e124"},
 ]
 
 [package.dependencies]
@@ -3160,25 +3245,21 @@ tutorials = ["matplotlib", "pandas", "tabulate", "torch"]
 
 [[package]]
 name = "typer"
-version = "0.9.0"
+version = "0.12.3"
 description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
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+    {file = "typer-0.12.3-py3-none-any.whl", hash = "sha256:070d7ca53f785acbccba8e7d28b08dcd88f79f1fbda035ade0aecec71ca5c914"},
+    {file = "typer-0.12.3.tar.gz", hash = "sha256:49e73131481d804288ef62598d97a1ceef3058905aa536a1134f90891ba35482"},
 ]
 
 [package.dependencies]
-click = ">=7.1.1,<9.0.0"
+click = ">=8.0.0"
+rich = ">=10.11.0"
+shellingham = ">=1.3.0"
 typing-extensions = ">=3.7.4.3"
 
-[package.extras]
-all = ["colorama (>=0.4.3,<0.5.0)", "rich (>=10.11.0,<14.0.0)", "shellingham (>=1.3.0,<2.0.0)"]
-dev = ["autoflake (>=1.3.1,<2.0.0)", "flake8 (>=3.8.3,<4.0.0)", "pre-commit (>=2.17.0,<3.0.0)"]
-doc = ["cairosvg (>=2.5.2,<3.0.0)", "mdx-include (>=1.4.1,<2.0.0)", "mkdocs (>=1.1.2,<2.0.0)", "mkdocs-material (>=8.1.4,<9.0.0)", "pillow (>=9.3.0,<10.0.0)"]
-test = ["black (>=22.3.0,<23.0.0)", "coverage (>=6.2,<7.0)", "isort (>=5.0.6,<6.0.0)", "mypy (==0.910)", "pytest (>=4.4.0,<8.0.0)", "pytest-cov (>=2.10.0,<5.0.0)", "pytest-sugar (>=0.9.4,<0.10.0)", "pytest-xdist (>=1.32.0,<4.0.0)", "rich (>=10.11.0,<14.0.0)", "shellingham (>=1.3.0,<2.0.0)"]
-
 [[package]]
 name = "types-attrs"
 version = "19.1.0"
@@ -3191,46 +3272,46 @@ files = [
 
 [[package]]
 name = "types-pyyaml"
-version = "6.0.12.12"
+version = "6.0.12.20240311"
 description = "Typing stubs for PyYAML"
 optional = false
-python-versions = "*"
+python-versions = ">=3.8"
 files = [
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-    {file = "types_PyYAML-6.0.12.12-py3-none-any.whl", hash = "sha256:c05bc6c158facb0676674b7f11fe3960db4f389718e19e62bd2b84d6205cfd24"},
+    {file = "types-PyYAML-6.0.12.20240311.tar.gz", hash = "sha256:a9e0f0f88dc835739b0c1ca51ee90d04ca2a897a71af79de9aec5f38cb0a5342"},
+    {file = "types_PyYAML-6.0.12.20240311-py3-none-any.whl", hash = "sha256:b845b06a1c7e54b8e5b4c683043de0d9caf205e7434b3edc678ff2411979b8f6"},
 ]
 
 [[package]]
 name = "typing-extensions"
-version = "4.9.0"
+version = "4.11.0"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "typing_extensions-4.9.0.tar.gz", hash = "sha256:23478f88c37f27d76ac8aee6c905017a143b0b1b886c3c9f66bc2fd94f9f5783"},
+    {file = "typing_extensions-4.11.0-py3-none-any.whl", hash = "sha256:c1f94d72897edaf4ce775bb7558d5b79d8126906a14ea5ed1635921406c0387a"},
+    {file = "typing_extensions-4.11.0.tar.gz", hash = "sha256:83f085bd5ca59c80295fc2a82ab5dac679cbe02b9f33f7d83af68e241bea51b0"},
 ]
 
 [[package]]
 name = "tzdata"
-version = "2023.4"
+version = "2024.1"
 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
 files = [
-    {file = "tzdata-2023.4-py2.py3-none-any.whl", hash = "sha256:aa3ace4329eeacda5b7beb7ea08ece826c28d761cda36e747cfbf97996d39bf3"},
-    {file = "tzdata-2023.4.tar.gz", hash = "sha256:dd54c94f294765522c77399649b4fefd95522479a664a0cec87f41bebc6148c9"},
+    {file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"},
+    {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
 ]
 
 [[package]]
 name = "urllib3"
-version = "2.2.0"
+version = "2.2.2"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "urllib3-2.2.0-py3-none-any.whl", hash = "sha256:ce3711610ddce217e6d113a2732fafad960a03fd0318c91faa79481e35c11224"},
-    {file = "urllib3-2.2.0.tar.gz", hash = "sha256:051d961ad0c62a94e50ecf1af379c3aba230c66c710493493560c0c223c49f20"},
+    {file = "urllib3-2.2.2-py3-none-any.whl", hash = "sha256:a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472"},
+    {file = "urllib3-2.2.2.tar.gz", hash = "sha256:dd505485549a7a552833da5e6063639d0d177c04f23bc3864e41e5dc5f612168"},
 ]
 
 [package.extras]
@@ -3241,13 +3322,13 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "wandb"
-version = "0.16.2"
+version = "0.16.6"
 description = "A CLI and library for interacting with the Weights & Biases API."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "wandb-0.16.2-py3-none-any.whl", hash = "sha256:6b119cf3c01f35e7276b62d052128e5320621d182c9eb5796a12cf62a9b3134f"},
-    {file = "wandb-0.16.2.tar.gz", hash = "sha256:e40cd79ea6272fe4762a80b9f47b172e141daeb3b56eb9d1e192ebd10752e64e"},
+    {file = "wandb-0.16.6-py3-none-any.whl", hash = "sha256:5810019a3b981c796e98ea58557a7c380f18834e0c6bdaed15df115522e5616e"},
+    {file = "wandb-0.16.6.tar.gz", hash = "sha256:86f491e3012d715e0d7d7421a4d6de41abef643b7403046261f962f3e512fe1c"},
 ]
 
 [package.dependencies]
@@ -3273,11 +3354,13 @@ async = ["httpx (>=0.23.0)"]
 aws = ["boto3"]
 azure = ["azure-identity", "azure-storage-blob"]
 gcp = ["google-cloud-storage"]
+importers = ["filelock", "mlflow", "polars", "rich", "tenacity"]
 kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
-launch = ["PyYAML (>=6.0.0)", "awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "typing-extensions"]
+launch = ["PyYAML (>=6.0.0)", "awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "tomli", "typing-extensions"]
 media = ["bokeh", "moviepy", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit-pypi", "soundfile"]
 models = ["cloudpickle"]
 perf = ["orjson"]
+reports = ["pydantic (>=2.0.0)"]
 sweeps = ["sweeps (>=0.2.0)"]
 
 [[package]]
@@ -3396,20 +3479,20 @@ multidict = ">=4.0"
 
 [[package]]
 name = "zipp"
-version = "3.17.0"
+version = "3.19.2"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"},
-    {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"},
+    {file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"},
+    {file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"},
 ]
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8,<3.13"
-content-hash = "41d52b4a8afee9799c88139eb283ae0ef90e7e4ec45b7ece985ea39a5cc401d9"
+content-hash = "cc43ee6bee96646cc0d229a9e799eddce955b40eacc8c8e746bb5d02165fa3db"
diff --git a/pyproject.toml b/pyproject.toml
index 1a716c7c..ba1cfb1a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "kwja"
-version = "2.3.0"
+version = "2.4.0"
 description = "A unified Japanese analyzer based on foundation models"
 authors = [
     "Hirokazu Kiyomaru <kiyomaru@i.kyoto-u.ac.jp>",
@@ -53,7 +53,7 @@ numpy = [
     { version = "~1.24.4", python = "<3.9" },
     { version = ">=1.26", python = ">=3.9" },
 ]
-transformers = { version = "~4.36.2", extras = ["sentencepiece"] }
+transformers = { version = "~4.38.2", extras = ["sentencepiece"] }
 tokenizers = "^0.15.1"
 pytorch-lightning = "~2.1.0"  # As "lightning" comes with many extra dependencies, we stay with "pytorch-lightning"
 omegaconf = "^2.1"
diff --git a/scripts/analyze.py b/scripts/analyze.py
index 318b52d5..3ae580eb 100644
--- a/scripts/analyze.py
+++ b/scripts/analyze.py
@@ -1,5 +1,6 @@
 import os
 import sys
+import tempfile
 from pathlib import Path
 from time import time
 from typing import List
@@ -27,6 +28,10 @@ def main(eval_cfg: DictConfig):
         eval_cfg.devices = (
             list(map(int, eval_cfg.devices.split(","))) if "," in eval_cfg.devices else int(eval_cfg.devices)
         )
+    if isinstance(eval_cfg.max_batches_per_device, str):
+        eval_cfg.max_batches_per_device = int(eval_cfg.max_batches_per_device)
+    if isinstance(eval_cfg.num_workers, str):
+        eval_cfg.num_workers = int(eval_cfg.num_workers)
 
     # Load saved model and config
     model: pl.LightningModule = hydra.utils.call(eval_cfg.module.load_from_checkpoint, _recursive_=False)
@@ -56,12 +61,20 @@ def main(eval_cfg: DictConfig):
         devices=cfg.devices,
     )
 
-    if getattr(cfg.datamodule.predict, "juman_file", None):
-        cfg.datamodule.predict.juman_file = Path(cfg.datamodule.predict.juman_file)
-    elif getattr(cfg.datamodule.predict, "texts", None) is not None:
-        cfg.datamodule.predict.texts = [normalize_text(line) for line in sys.stdin.readlines()]
-    datamodule = DataModule(cfg=cfg.datamodule)
-    datamodule.setup(stage=TrainerFn.PREDICTING)
+    with tempfile.NamedTemporaryFile(mode="w+t") as temp_file:
+        if juman_file := getattr(cfg.datamodule.predict, "juman_file", None):
+            # For seq2seq module and word module
+            cfg.datamodule.predict.juman_file = Path(juman_file)
+        elif raw_input_file := getattr(cfg.datamodule.predict, "raw_input_file", None):
+            # For typo module and char module
+            cfg.datamodule.predict.raw_text_file = Path(raw_input_file)
+        else:
+            # For typo module and char module
+            Path(temp_file.name).write_text("\n".join(normalize_text(line) for line in sys.stdin.readlines()))
+            cfg.datamodule.predict.raw_text_file = Path(temp_file.name)
+
+        datamodule = DataModule(cfg=cfg.datamodule)
+        datamodule.setup(stage=TrainerFn.PREDICTING)
 
     trainer.predict(model=model, dataloaders=[datamodule.predict_dataloader()], return_predictions=False)
 
diff --git a/scripts/benchmark.sh b/scripts/benchmark.sh
index 839d4933..f7599881 100755
--- a/scripts/benchmark.sh
+++ b/scripts/benchmark.sh
@@ -106,10 +106,10 @@ echo "Juman++ & KNP (NER + Dependency parsing + PAS analysis)"
 grep "# S-ID:" "$WORK_DIR/benchmark.knp_ne_anaphora.knp" | grep -cv "ERROR:" >> "$WORK_DIR/count.txt"
 
 echo "KWJA (typo_module)"
-cat "$INPUT" | poetry run python ./scripts/analyze.py module=typo checkpoint_path="$TYPO_MODULE" devices="$DEVICE" max_batches_per_device="$TYPO_BATCH_SIZE" > "$WORK_DIR/benchmark.kwja.txt" 2>> "$WORK_DIR/benchmark.stderr"
+poetry run python ./scripts/analyze.py module=typo checkpoint_path="$TYPO_MODULE" devices="$DEVICE" max_batches_per_device="$TYPO_BATCH_SIZE" +datamodule.predict.raw_input_file="$INPUT" > "$WORK_DIR/benchmark.kwja.txt" 2>> "$WORK_DIR/benchmark.stderr"
 
 echo "KWJA (char_module)"
-cat "$INPUT" | poetry run python ./scripts/analyze.py module=char checkpoint_path="$CHAR_MODULE" devices="$DEVICE" max_batches_per_device="$CHAR_BATCH_SIZE" > "$WORK_DIR/benchmark.kwja.juman" 2>> "$WORK_DIR/benchmark.stderr"
+poetry run python ./scripts/analyze.py module=char checkpoint_path="$CHAR_MODULE" devices="$DEVICE" max_batches_per_device="$CHAR_BATCH_SIZE" +datamodule.predict.raw_input_file="$INPUT" > "$WORK_DIR/benchmark.kwja.juman" 2>> "$WORK_DIR/benchmark.stderr"
 grep "# S-ID:" "$WORK_DIR/benchmark.kwja.juman" | cut -f -3 -d "-" | uniq | wc -l >> "$WORK_DIR/count.txt"
 
 echo "KWJA (word_module)"
diff --git a/scripts/predict.py b/scripts/predict.py
index 885d99ac..592a86d0 100644
--- a/scripts/predict.py
+++ b/scripts/predict.py
@@ -23,6 +23,10 @@ def main(eval_cfg: DictConfig):
         eval_cfg.devices = (
             list(map(int, eval_cfg.devices.split(","))) if "," in eval_cfg.devices else int(eval_cfg.devices)
         )
+    if isinstance(eval_cfg.max_batches_per_device, str):
+        eval_cfg.max_batches_per_device = int(eval_cfg.max_batches_per_device)
+    if isinstance(eval_cfg.num_workers, str):
+        eval_cfg.num_workers = int(eval_cfg.num_workers)
 
     # Load saved model and config
     model: pl.LightningModule = hydra.utils.call(eval_cfg.module.load_from_checkpoint, _recursive_=False)
diff --git a/scripts/preprocessors/preprocess_reading.py b/scripts/preprocessors/preprocess_reading.py
new file mode 100644
index 00000000..fbd3ac6a
--- /dev/null
+++ b/scripts/preprocessors/preprocess_reading.py
@@ -0,0 +1,44 @@
+import logging
+from argparse import ArgumentParser
+from collections import Counter
+from pathlib import Path
+from typing import Dict
+
+from rhoknp import Document
+from transformers import AutoTokenizer
+
+from kwja.utils.kanjidic import KanjiDic
+from kwja.utils.reading_prediction import ReadingAligner
+
+logger = logging.getLogger(__name__)
+
+
+def main():
+    parser = ArgumentParser()
+    parser.add_argument("-m", "--model-name-or-path", type=str, help="model_name_or_path")
+    parser.add_argument("-k", "--kanji-dic", type=str, help="path to kanji dic file")
+    parser.add_argument("-i", "--in-dir", type=Path, help="path to input directory")
+    args = parser.parse_args()
+
+    tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
+    kanji_dic = KanjiDic(args.kanji_dic)
+    reading_aligner = ReadingAligner(tokenizer, kanji_dic)
+
+    reading_counter: Dict[str, int] = Counter()
+    for path in args.in_dir.glob("**/*.knp"):
+        logger.info(f"processing {path}")
+        with path.open() as f:
+            document = Document.from_knp(f.read())
+        try:
+            for reading in reading_aligner.align(document.morphemes):
+                reading_counter[reading] += 1
+        except ValueError:
+            logger.warning(f"skip {document.doc_id} for an error")
+    for subreading, count in sorted(
+        sorted(reading_counter.items(), key=lambda pair: pair[0]), key=lambda pair: pair[1], reverse=True
+    ):
+        print(f"{subreading}\t{count}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/preprocessors/preprocess_typo.py b/scripts/preprocessors/preprocess_typo.py
index 0af3a6a7..8be5b6e8 100644
--- a/scripts/preprocessors/preprocess_typo.py
+++ b/scripts/preprocessors/preprocess_typo.py
@@ -2,268 +2,250 @@
 import logging
 import random
 import re
-from argparse import ArgumentParser
+from argparse import ArgumentParser, RawTextHelpFormatter
 from collections import Counter, defaultdict
 from dataclasses import dataclass
 from enum import Enum
 from pathlib import Path
+from textwrap import dedent
 from typing import Dict, List, Optional, Tuple
 
 from Levenshtein import opcodes
 
+from kwja.utils.normalization import normalize_text
+
 logger = logging.getLogger(__name__)
 logging.basicConfig(format="%(asctime)s - %(name)s - %(levelname)s: %(message)s", level=logging.DEBUG)
 
 
 @dataclass(frozen=True)
-class TypoDiff:
+class Component:
     pre_str: str
     post_str: str
+    type: str
 
 
-@dataclass(frozen=True)
-class Block:
-    text: str
-    diff: Optional[TypoDiff]
-
-
-class OpcodeType(Enum):
+class OpType(Enum):
     EQUAL = "equal"
     DELETE = "delete"
     INSERT = "insert"
     REPLACE = "replace"
 
 
-class TypoPreprocessor:
-    def __init__(self):
-        pass
-
-    @staticmethod
-    def decompose(pre_text: str, post_text: str, typo_diffs: List[TypoDiff]) -> Optional[List[Block]]:
-        # decompose pre_text into blocks
-        # return None if alignment fails
-        assert len(typo_diffs) > 0
-
-        s_idx: int = 0
-        remaining_pre_text: str = pre_text
-        remaining_post_text: str = post_text
-        blocks: List[Block] = []
-        for diff in typo_diffs:
-            if len(diff.pre_str) > 0 and len(diff.post_str) > 0:
-                # both pre_str and post_str are non-null: check if post_text contains post_str at the position s_idx
-                for m in re.finditer(re.escape(diff.pre_str), remaining_pre_text):
-                    s_idx = m.start()
-                    if remaining_post_text[s_idx : s_idx + len(diff.post_str)] == diff.post_str:
-                        if s_idx + len(diff.pre_str) < len(remaining_pre_text):
-                            pre_idx = s_idx + len(diff.pre_str)
-                            post_idx = s_idx + len(diff.post_str)
-                            if (
-                                remaining_pre_text[pre_idx : pre_idx + 1]
-                                != remaining_post_text[post_idx : post_idx + 1]
-                            ):
-                                continue
-                        if remaining_pre_text[:s_idx] != remaining_post_text[:s_idx]:
-                            logger.warning("remaining_pre_text != remaining_post_text...skip")
-                            return None
-                        blocks.append(Block(text=remaining_pre_text[:s_idx], diff=None))
-                        blocks.append(Block(text="", diff=TypoDiff(pre_str=diff.pre_str, post_str=diff.post_str)))
-                        remaining_pre_text = remaining_pre_text[s_idx + len(diff.pre_str) :]
-                        remaining_post_text = remaining_post_text[s_idx + len(diff.post_str) :]
-                        break
-                else:
-                    logger.warning(f"pre_str {diff.pre_str} not found...skip")
-                    return None
-            elif len(diff.pre_str) > 0:
-                # pre_str is non-null and post_str is null: check if the preceding sentences + the next chars (if any) are the same
-                for m in re.finditer(re.escape(diff.pre_str), remaining_pre_text):
-                    s_idx = m.start()
-                    pre_fragment: str = remaining_pre_text[:s_idx]
-                    post_fragment: str = remaining_post_text[: len(pre_fragment)]
-                    if s_idx + len(diff.pre_str) < len(remaining_pre_text):
-                        post_fragment += remaining_post_text[len(pre_fragment) : len(pre_fragment) + 1]
-                        pre_idx = s_idx + len(diff.pre_str)
-                        pre_fragment += remaining_pre_text[pre_idx : pre_idx + 1]
-                    if pre_fragment == post_fragment:
-                        blocks.append(Block(text=remaining_pre_text[:s_idx], diff=None))
-                        blocks.append(Block(text="", diff=TypoDiff(pre_str=diff.pre_str, post_str=diff.post_str)))
-                        remaining_pre_text = remaining_pre_text[s_idx + len(diff.pre_str) :]
-                        remaining_post_text = remaining_post_text[s_idx:]
-                        break
-                else:
-                    logger.warning(f"pre_str {diff.pre_str} not found...skip")
-                    return None
-            elif len(diff.post_str) > 0:
-                for m in re.finditer(re.escape(diff.post_str), remaining_post_text):
-                    s_idx = m.start()
-                    post_fragment = remaining_post_text[:s_idx] + diff.pre_str
-                    pre_fragment = remaining_pre_text[: len(post_fragment)]
-                    if s_idx + len(diff.post_str) < len(remaining_post_text):
-                        pre_fragment += remaining_pre_text[len(post_fragment) : len(post_fragment) + 1]
-                        post_pos = s_idx + len(diff.post_str)
-                        post_fragment += remaining_post_text[post_pos : post_pos + 1]
-                    if pre_fragment == post_fragment:
-                        blocks.append(Block(text=remaining_post_text[:s_idx], diff=None))
-                        blocks.append(Block(text="", diff=TypoDiff(pre_str=diff.pre_str, post_str=diff.post_str)))
-                        remaining_pre_text = remaining_pre_text[s_idx:]
-                        remaining_post_text = remaining_post_text[s_idx + len(diff.post_str) :]
-                        break
-                else:
-                    logger.warning(f"post_str {diff.post_str} not found...skip")
-                    return None
-            else:
-                logger.warning("broken diff...skip")
-                return None
+def normalize_example(example: dict) -> None:
+    example["pre_text"] = normalize_text(example["pre_text"])
+    example["post_text"] = normalize_text(example["post_text"])
+    for diff in example["diffs"]:
+        diff["pre_str"] = normalize_text(diff["pre_str"])
+        diff["post_str"] = normalize_text(diff["post_str"])
+
 
-        if len(remaining_pre_text) > 0:
-            if remaining_pre_text != remaining_post_text:
-                logger.warning("remaining_pre_text != remaining_post_text...skip")
+def decompose(pre_text: str, post_text: str, diffs: List[dict]) -> Optional[List[Component]]:
+    # decompose texts into components
+    # return None if alignment fails
+    components: List[Component] = []
+    monitor: Dict[str, str] = {"pre_text": pre_text, "post_text": post_text}
+    for diff in diffs:
+        if len(diff["pre_str"]) == 0:
+            src, tgt = "post", "pre"
+            type_ = "insert"
+        else:
+            src, tgt = "pre", "post"
+            type_ = "delete" if len(diff["post_str"]) == 0 else "replace"
+        for mo in re.finditer(re.escape(diff[f"{src}_str"]), monitor[f"{src}_text"]):
+            start = mo.start()
+
+            src_same = monitor[f"{src}_text"][:start]
+            tgt_same = monitor[f"{tgt}_text"][:start]
+            if src_same != tgt_same:  # diffs以外にもtypoを含んでいる
+                logger.warning(f'"{monitor[f"pre_text"]}" != "{monitor[f"post_text"]}" ... skip')
                 return None
-            blocks.append(Block(text=remaining_pre_text[:s_idx], diff=None))
-        return blocks
-
-    @staticmethod
-    def generate_opn(pre_text: str, blocks: List[Block]) -> Tuple[List[str], List[str]]:
-        # Keep ("K"), Delete ("D"), Replace ("R:x")
-        kdr_opns: List[str] = ["K"] * (len(pre_text) + 1)
-        # Insert ("I:x"), Nothing ("_")
-        ins_opns: List[str] = ["_"] * (len(pre_text) + 1)
-
-        s_idx = 0
-        for block in blocks:
-            if block.diff is None:  # keep all
-                s_idx += len(block.text)
+            src_diff = monitor[f"{src}_text"][start : start + len(diff[f"{src}_str"])]
+            tgt_diff = monitor[f"{tgt}_text"][start : start + len(diff[f"{tgt}_str"])]
+            if tgt_diff != diff[f"{tgt}_str"]:  # diff[f"{src}_str"]がsrc_textのtypoじゃないところにマッチしている
+                continue
+            src_cont = monitor[f"{src}_text"][start + len(diff[f"{src}_str"]) : start + len(diff[f"{src}_str"]) + 1]
+            tgt_cont = monitor[f"{tgt}_text"][start + len(diff[f"{tgt}_str"]) : start + len(diff[f"{tgt}_str"]) + 1]
+            if src_cont != tgt_cont:  # diffの続きが一致しない = typoじゃないところにマッチしている
+                continue
+
+            if len(diff["pre_str"]) == 0:
+                components.append(Component(pre_str=tgt_same, post_str=src_same, type="equal"))
+                components.append(Component(pre_str=tgt_diff, post_str=src_diff, type=type_))
             else:
-                diff = block.diff
-                if len(diff.pre_str) == 0:  # insert post_str
-                    assert ins_opns[s_idx] == "_"
-                    ins_opns[s_idx] = f"I:{diff.post_str}"
-                elif len(diff.post_str) == 0:  # delete pre_str
-                    for i in range(len(diff.pre_str)):
-                        kdr_opns[s_idx + i] = "D"
-                    s_idx += len(diff.pre_str)
-                else:
-                    for opn, i1, i2, j1, j2 in opcodes(diff.pre_str, diff.post_str):
-                        if OpcodeType(opn) == OpcodeType.EQUAL:
-                            pass
-                        elif OpcodeType(opn) == OpcodeType.DELETE:
-                            for i in range(i1, i2):
-                                kdr_opns[s_idx + i] = "D"
-                        elif OpcodeType(opn) == OpcodeType.INSERT:
-                            ins_opns[s_idx + i1] = f"I:{diff.post_str[j1:j2]}"
-                        elif OpcodeType(opn) == OpcodeType.REPLACE:
-                            if i2 - i1 == j2 - j1:
-                                for i in range(i2 - i1):
-                                    kdr_opns[s_idx + i1 + i] = f"R:{diff.post_str[j1 + i]}"
-                            else:
-                                logger.warning(f"replace\t{diff.pre_str[i1:i2]}\t{diff.post_str[j1:j2]}\n")
+                components.append(Component(pre_str=src_same, post_str=tgt_same, type="equal"))
+                components.append(Component(pre_str=src_diff, post_str=tgt_diff, type=type_))
+            monitor.update(
+                {
+                    "pre_text": monitor["pre_text"][start + len(diff["pre_str"]) :],
+                    "post_text": monitor["post_text"][start + len(diff["post_str"]) :],
+                }
+            )
+            break
+    if len(monitor["pre_text"]) > 0 and len(monitor["post_text"]) > 0:
+        if monitor["pre_text"] != monitor["post_text"]:  # diffs以外にもtypoを含んでいる
+            logger.warning(f'"{monitor[f"pre_text"]}" != "{monitor[f"post_text"]}" ... skip')
+            return None
+        components.append(Component(pre_str=monitor["pre_text"], post_str=monitor["post_text"], type="equal"))
+    return components
+
+
+def convert_components_into_tags(components: List[Component], length: int) -> Tuple[List[str], List[str]]:
+    # Keep ("K"), Delete ("D"), Replace ("R:x")
+    kdr_tags: List[str] = ["K"] * length
+    # Insert ("I:x"), Nothing ("_")
+    ins_tags: List[str] = ["_"] * length
+
+    cursor = 0
+    for component in components:
+        if component.type == "equal":  # keep all
+            cursor += len(component.pre_str)
+        else:
+            if component.type == "delete":
+                for i in range(len(component.pre_str)):
+                    kdr_tags[cursor + i] = "D"
+            elif component.type == "insert":
+                assert ins_tags[cursor] == "_"
+                ins_tags[cursor] = f"I:{component.post_str}"
+            elif component.type == "replace":
+                for op, i1, i2, j1, j2 in opcodes(component.pre_str, component.post_str):
+                    if OpType(op) == OpType.EQUAL:
+                        pass
+                    elif OpType(op) == OpType.DELETE:
+                        for i in range(i1, i2):
+                            kdr_tags[cursor + i] = "D"
+                    elif OpType(op) == OpType.INSERT:
+                        ins_tags[cursor + i1] = f"I:{component.post_str[j1:j2]}"
+                    elif OpType(op) == OpType.REPLACE:
+                        if i2 - i1 == j2 - j1:
+                            for i in range(i2 - i1):
+                                kdr_tags[cursor + i1 + i] = f"R:{component.post_str[j1 + i]}"
                         else:
-                            raise ValueError("unsupported operation!")
-                    s_idx += len(diff.pre_str)
-        return kdr_opns, ins_opns
-
-    @staticmethod
-    def make_multi_char_vocab(train_path: Path, output_dir: Path) -> None:
-        multi_char_vocabs: List[str] = []
-        with train_path.open(mode="r") as fr:
-            for line in fr:
-                train_example: dict = json.loads(line)
-                for ins_opn in train_example["inss"]:
-                    if ins_opn == "_":
-                        continue
-                    ins_vocab: str = ins_opn.removeprefix("I:")
-                    if len(ins_vocab) > 1:
-                        multi_char_vocabs.append(ins_vocab)
-
-        multi_char_vocabs_counter = Counter(multi_char_vocabs)
-
-        with output_dir.joinpath("multi_char_vocab.txt").open("w") as fw:
-            for vocab, count in multi_char_vocabs_counter.most_common():
-                if count > 1:
-                    fw.write(f"{vocab}\n")
+                            logger.warning(f"replace\t{component.pre_str[i1:i2]}\t{component.post_str[j1:j2]}\n")
+                    else:
+                        raise ValueError("invalid operation")
+            else:
+                raise ValueError("invalid operation")
+            cursor += len(component.pre_str)
+    return kdr_tags, ins_tags
+
+
+def load_examples(in_dir: Path, split: str) -> Tuple[Dict[str, List[Dict[str, str]]], List[Dict[str, str]]]:
+    category2examples: Dict[str, List[Dict[str, str]]] = defaultdict(list)
+    other_examples: List[Dict[str, str]] = []
+    with (in_dir / f"{split}.jsonl").open() as f:
+        for line in f:
+            example: dict = json.loads(line)
+            normalize_example(example)
+            diffs = [diff for diff in example["diffs"] if diff["category"] != "not-typo"]
+            assert len(diffs) > 0
+            components: Optional[List[Component]] = decompose(example["pre_text"], example["post_text"], diffs)
+            if components is None:
+                continue
+            kdr_tags, ins_tags = convert_components_into_tags(components, len(example["pre_text"]) + 1)
+            example = {
+                "pre_text": example["pre_text"],
+                "post_text": example["post_text"],
+                "kdr_tags": kdr_tags,
+                "ins_tags": ins_tags,
+            }
+            if split == "train" and len(diffs) == 1:
+                category2examples[diffs[0]["category"]].append(example)
+            else:
+                other_examples.append(example)
+    return category2examples, other_examples
+
+
+def save_examples(
+    category2examples: Dict[str, List[Dict[str, str]]],
+    other_examples: List[Dict[str, str]],
+    out_dir: Path,
+    split: str,
+    num_valid_examples_per_category: int,
+) -> None:
+    if split == "train":
+        train_examples: List[Dict[str, str]] = other_examples
+        valid_examples: List[Dict[str, str]] = []
+        for category, examples in category2examples.items():
+            train_examples.extend(examples[num_valid_examples_per_category:])
+            valid_examples.extend(examples[:num_valid_examples_per_category])
+
+        random.shuffle(train_examples)
+        train_dir: Path = out_dir / split
+        train_dir.mkdir(parents=True, exist_ok=True)
+        with (train_dir / f"{split}.jsonl").open(mode="w") as f:
+            f.write("\n".join(json.dumps(e, ensure_ascii=False) for e in train_examples) + "\n")
+
+        valid_dir: Path = out_dir / "valid"
+        valid_dir.mkdir(parents=True, exist_ok=True)
+        with (valid_dir / "valid.jsonl").open(mode="w") as f:
+            f.write("\n".join(json.dumps(e, ensure_ascii=False) for e in valid_examples) + "\n")
+    elif split == "test":
+        test_dir: Path = out_dir / split
+        test_dir.mkdir(parents=True, exist_ok=True)
+        with (test_dir / f"{split}.jsonl").open(mode="w") as f:
+            f.write("\n".join(json.dumps(e, ensure_ascii=False) for e in other_examples) + "\n")
+    else:
+        raise ValueError("invalid split")
+
+
+def build_multi_char_vocab(out_dir: Path) -> None:
+    multi_char_vocab: List[str] = []
+    with (out_dir / "train" / "train.jsonl").open() as f:
+        for line in f:
+            train_example: dict = json.loads(line)
+            for ins_tag in train_example["ins_tags"]:
+                if ins_tag == "_":
+                    continue
+                ins_char: str = ins_tag.removeprefix("I:")
+                if len(ins_char) > 1:
+                    multi_char_vocab.append(ins_char)
+
+    multi_char_vocab_counter = Counter(multi_char_vocab)
+
+    with (out_dir / "multi_char_vocab.txt").open(mode="w") as f:
+        for vocab, count in multi_char_vocab_counter.most_common():
+            if count > 1:
+                f.write(f"{vocab}\n")
 
 
 def main():
-    parser = ArgumentParser()
+    parser = ArgumentParser(formatter_class=RawTextHelpFormatter)
     parser.add_argument(
         "-i",
-        "--input-dir",
-        type=str,
+        "--in-dir",
+        type=Path,
         help="path to input directory",
     )
     parser.add_argument(
         "-n",
-        "--num-valid-samples-per-category",
+        "--num-valid-examples-per-category",
         type=int,
         default=1000,
-        help="number of validation data. The evaluation data is extracted from the train.jsonl "
-        "by the number of num-valid-samples-per-category for each of the 8 error categories."
-        "Therefore, the actual number of evaluation data is 8 times the number specified here."
-        "The training data is the rest.",
+        help=dedent(
+            """\
+            number of validation examples. \
+            The validation data is extracted from the train.jsonl by the number of num-valid-examples-per-category for \
+            each of the 8 error categories. \
+            Therefore, the actual number of validation data is 8 times the number specified here. \
+            The training data is the rest.\
+            """
+        ),
     )
     parser.add_argument(
         "-o",
-        "--output-dir",
-        type=str,
-        default="./data",
-        help="path to directory to save",
+        "--out-dir",
+        type=Path,
+        help="path to output directory",
     )
     args = parser.parse_args()
 
-    preprocessor: TypoPreprocessor = TypoPreprocessor()
-    for filename in ["test", "train"]:
-        category2obj: Dict[str, List[str]] = defaultdict(list)
-        other_objs: List[str] = []
-        with Path(f"{args.input_dir}/{filename}.jsonl").open("r") as f:
-            for line in f:
-                example: dict = json.loads(line)
-                diffs = [diff for diff in example["diffs"] if diff["category"] != "not-typo"]
-                blocks: Optional[List[Block]] = preprocessor.decompose(
-                    pre_text=example["pre_text"],
-                    post_text=example["post_text"],
-                    typo_diffs=[TypoDiff(pre_str=diff["pre_str"], post_str=diff["post_str"]) for diff in diffs],
-                )
-                if blocks is None:
-                    continue
-                kdr_opns, ins_opns = preprocessor.generate_opn(pre_text=example["pre_text"], blocks=blocks)
-                obj = {
-                    "pre_text": example["pre_text"],
-                    "post_text": example["post_text"],
-                    "kdrs": kdr_opns,
-                    "inss": ins_opns,
-                }
-                if len(diffs) == 1 and filename == "train":
-                    category2obj[diffs[0]["category"]].append(json.dumps(obj, ensure_ascii=False))
-                else:
-                    other_objs.append(json.dumps(obj, ensure_ascii=False))
-
-        if filename == "test":
-            test_dir: Path = Path(f"{args.output_dir}/{filename}")
-            test_dir.mkdir(exist_ok=True)
-            with test_dir.joinpath(f"{filename}.jsonl").open("w") as f:
-                f.write("\n".join(other_objs) + "\n")
-        else:
-            valid_save_objs: List[str] = []
-            train_save_objs: List[str] = other_objs
-            for category, objs in category2obj.items():
-                random_objs = random.sample(objs, len(objs))
-                valid_save_objs.extend(random_objs[: args.num_valid_samples_per_category])
-                train_save_objs.extend(random_objs[args.num_valid_samples_per_category :])
-
-            valid_dir: Path = Path(f"{args.output_dir}/valid")
-            valid_dir.mkdir(exist_ok=True)
-            with valid_dir.joinpath("valid.jsonl").open("w") as f:
-                f.write("\n".join(valid_save_objs) + "\n")
-
-            random.shuffle(train_save_objs)
-            train_dir: Path = Path(f"{args.output_dir}/train")
-            train_dir.mkdir(exist_ok=True)
-            with train_dir.joinpath("train.jsonl").open("w") as f:
-                f.write("\n".join(train_save_objs) + "\n")
-
-    preprocessor.make_multi_char_vocab(
-        train_path=Path(f"{args.output_dir}/train/train.jsonl"),
-        output_dir=Path(args.output_dir),
-    )
+    random.seed(0)
+
+    for split in ["train", "test"]:
+        category2examples, other_examples = load_examples(args.in_dir, split)
+        save_examples(category2examples, other_examples, args.out_dir, split, args.num_valid_examples_per_category)
+    build_multi_char_vocab(args.out_dir)
 
 
 if __name__ == "__main__":
diff --git a/scripts/view_typo_results.py b/scripts/view_typo_results.py
deleted file mode 100644
index ae8c0c61..00000000
--- a/scripts/view_typo_results.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import json
-from argparse import ArgumentParser
-from pathlib import Path
-from typing import Dict, List, Tuple
-
-from Levenshtein import opcodes
-
-
-def apply_ops(pre_text: str, kdrs: List[str], inss: List[str]) -> str:
-    post_text = ""
-    for i, c in enumerate(pre_text):
-        if inss[i] != "_":
-            post_text += inss[i][2:]
-        if kdrs[i] == "K":
-            post_text += c
-        elif kdrs[i] == "D":
-            pass
-        elif len(kdrs[i]) > 2 and kdrs[i][0:2] == "R:":
-            post_text += kdrs[i][2:]
-        else:
-            raise ValueError("unsupported op")
-    if inss[-1] != "_":
-        post_text += inss[-1][2:]
-    return post_text
-
-
-def get_diffs(before: str, after: str) -> List[Tuple[str, str]]:
-    before_split: List[str] = list(before)
-    after_split: List[str] = list(after)
-
-    char2id: Dict[str, str] = dict()
-    id2char: Dict[str, str] = dict()
-    id_ = 0
-    for char in before_split + after_split:
-        if char not in char2id:
-            char2id[char] = chr(id_)
-            id2char[chr(id_)] = char
-            id_ += 1
-
-    before_id_string: str = "".join([char2id[i] for i in before_split])
-    after_id_string: str = "".join([char2id[i] for i in after_split])
-
-    diff_list: List[Tuple[str, str]] = []
-    for operation, bb, be, ab, ae in opcodes(before_id_string, after_id_string):
-        if operation == "insert":
-            for char_id in after_id_string[ab:ae]:
-                diff_list.append(("", id2char[char_id]))
-        if operation == "delete":
-            for char_id in before_id_string[bb:be]:
-                diff_list.append((id2char[char_id], ""))
-        if operation == "replace":
-            assert be - bb == ae - ab, (before_split[bb:be], after_split[ab:ae])
-            for before_char_id, after_char_id in zip(before_id_string[bb:be], after_id_string[ab:ae]):
-                diff_list.append((id2char[before_char_id], id2char[after_char_id]))
-    return diff_list
-
-
-def calc_tp_fp_fn(pre_text: str, ref_post_text: str, pred_post_text: str):
-    ref_diffs: List[Tuple[str, str]] = get_diffs(pre_text, ref_post_text)
-    pred_diffs: List[Tuple[str, str]] = get_diffs(pre_text, pred_post_text)
-
-    ref_num: int = len(ref_diffs)
-    tp: int = 0
-    for diff in pred_diffs:
-        if diff in ref_diffs:
-            tp += 1
-            ref_diffs.remove(diff)
-    fp: int = len(pred_diffs) - tp
-    fn: int = ref_num - tp
-    assert tp >= 0 and fp >= 0 and fn >= 0, (
-        tp,
-        fp,
-        fn,
-        get_diffs(pre_text, ref_post_text),
-        get_diffs(pre_text, pred_post_text),
-    )
-
-    return tp, fp, fn
-
-
-def main():
-    parser = ArgumentParser()
-    parser.add_argument("-p", "--path", type=str, required=True, help="path to prediction file")
-    args = parser.parse_args()
-
-    with Path(args.path).open() as f:
-        preds: dict = json.load(f)
-
-    tp_total, fp_total, fn_total = 0, 0, 0
-    for example_id, pred in preds.items():
-        pre_text: str = pred["input_ids"].replace(" ", "")
-        pre_text = pre_text.replace("...", "…")
-        ref_post_text: str = apply_ops(pre_text=pre_text, kdrs=pred["kdr_labels"] + ["K"], inss=pred["ins_labels"])
-        pred_post_text: str = apply_ops(pre_text=pre_text, kdrs=pred["kdr_preds"] + ["K"], inss=pred["ins_preds"])
-        tp, fp, fn = calc_tp_fp_fn(
-            pre_text=pre_text,
-            ref_post_text=ref_post_text,
-            pred_post_text=pred_post_text,
-        )
-        tp_total += tp
-        fp_total += fp
-        fn_total += fn
-
-    precision: float = tp_total / (tp_total + fp_total)
-    recall: float = tp_total / (tp_total + fn_total)
-    if precision + recall != 0:
-        f1 = (2 * precision * recall) / (precision + recall)
-        f05 = (1 + 0.5**2) * precision * recall / (recall + (0.5**2) * precision)
-    else:
-        f1, f05 = 0, 0
-
-    print("Precision, Recall, F-score, F0.5-score")
-    print(
-        round(precision * 100, 1),
-        "\t",
-        round(recall * 100, 1),
-        "\t",
-        round(f1 * 100, 1),
-        "\t",
-        round(f05 * 100, 1),
-        "\t",
-    )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/src/kwja/callbacks/char_module_writer.py b/src/kwja/callbacks/char_module_writer.py
index d3a1707c..3bcca69b 100644
--- a/src/kwja/callbacks/char_module_writer.py
+++ b/src/kwja/callbacks/char_module_writer.py
@@ -32,7 +32,11 @@ def write_on_batch_end(
         else:
             dataloader = trainer.predict_dataloaders[dataloader_idx]
         dataset: Union[CharDataset, CharInferenceDataset] = dataloader.dataset
-        special_ids = set(dataset.tokenizer.all_special_ids) - {dataset.tokenizer.unk_token_id}
+
+        special_ids = {
+            getattr(dataset.tokenizer, f"{prefix}_token_id") for prefix in ["bos", "eos", "sep", "pad", "cls", "mask"]
+        }
+
         for example_id, sent_segmentation_predictions, word_segmentation_predictions, word_norm_op_predictions in zip(
             *[v.tolist() for v in prediction.values()]
         ):
diff --git a/src/kwja/callbacks/utils.py b/src/kwja/callbacks/utils.py
index c781eb51..e1d47889 100644
--- a/src/kwja/callbacks/utils.py
+++ b/src/kwja/callbacks/utils.py
@@ -30,7 +30,7 @@
     CohesionTask,
 )
 from kwja.utils.dependency_parsing import DependencyManager
-from kwja.utils.word_normalization import get_normalized
+from kwja.utils.word_normalization import get_normalized_surf
 
 logger = getLogger(__name__)
 
@@ -100,23 +100,25 @@ def set_sentences(document: Document, sent_segmentation_tags: List[str]) -> None
 
 
 def set_morphemes(document: Document, word_segmentation_tags: List[str], word_norm_op_tags: List[str]) -> None:
-    char_index = 0
+    cursor = 0
     for sentence in document.sentences:
         Morpheme.count = 0
         morphemes: List[Morpheme] = []
-        surf: str = ""
-        ops: List[str] = []
+        surf = ""
         for char in sentence.text:
-            if word_segmentation_tags[char_index] == "B" and surf:
-                norm = get_normalized(surf, ops, strict=False)
+            # 半角スペースで強制分割
+            if (word_segmentation_tags[cursor] == "B" or char == " ") and surf:
+                norm = get_normalized_surf(surf, word_norm_op_tags[cursor - len(surf) : cursor], strict=False)
                 morphemes.append(_build_morpheme(surf, norm))
                 surf = ""
-                ops = []
-            surf += char
-            ops.append(word_norm_op_tags[char_index])
-            char_index += 1
+
+            if char == " ":
+                morphemes.append(_build_morpheme(char, char))
+            else:
+                surf += char
+            cursor += 1
         if surf:
-            norm = get_normalized(surf, ops, strict=False)
+            norm = get_normalized_surf(surf, word_norm_op_tags[cursor - len(surf) : cursor], strict=False)
             morphemes.append(_build_morpheme(surf, norm))
         sentence.morphemes = morphemes
 
diff --git a/src/kwja/cli/cli.py b/src/kwja/cli/cli.py
index e77f4098..56dc00da 100644
--- a/src/kwja/cli/cli.py
+++ b/src/kwja/cli/cli.py
@@ -1,6 +1,5 @@
 import logging
 import os
-import re
 import sys
 from abc import ABC
 from enum import Enum
@@ -29,7 +28,6 @@
 
 filter_logs(environment="production")
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
-OMEGACONF_VARIABLE_INTERPOLATION = re.compile(r"\$(?P<variable>\{.*?})")
 logging.basicConfig(format="")
 
 logger = logging.getLogger("kwja_cli")
@@ -102,8 +100,7 @@ def _load_module(self) -> pl.LightningModule:
 
     def _load_datamodule(self, input_file: Path) -> DataModule:
         assert self.module is not None
-        with input_file.open() as f:
-            self.module.hparams.datamodule.predict.texts = list(_chunk_by_document(f, self.input_format))
+        self.module.hparams.datamodule.predict.raw_text_file = input_file
         datamodule = DataModule(cfg=self.module.hparams.datamodule)
         datamodule.setup(stage=TrainerFn.PREDICTING)
         return datamodule
@@ -128,8 +125,7 @@ def _load_module(self) -> pl.LightningModule:
 
     def _load_datamodule(self, input_file: Path) -> DataModule:
         assert self.module is not None
-        with input_file.open() as f:
-            self.module.hparams.datamodule.predict.texts = list(_chunk_by_document(f, self.input_format))
+        self.module.hparams.datamodule.predict.raw_text_file = input_file
         datamodule = DataModule(cfg=self.module.hparams.datamodule)
         datamodule.setup(stage=TrainerFn.PREDICTING)
         return datamodule
@@ -248,8 +244,6 @@ def normalize_text(text: str) -> str:
     normalized = normalize("NFKC", text)
     # escape several symbols and delete control characters
     normalized = normalized.translate(TRANSLATION_TABLE)
-    # prevent hydra.utils.instantiate from interpolating the string "${...}"
-    normalized = OMEGACONF_VARIABLE_INTERPOLATION.sub(r"$␣\g<variable>", normalized)
     # if normalized != text:
     #     typer.echo(f"apply normalization ({text} -> {normalized})", err=True)
     return normalized
diff --git a/src/kwja/cli/utils.py b/src/kwja/cli/utils.py
index 8cbbf06a..583ada1b 100644
--- a/src/kwja/cli/utils.py
+++ b/src/kwja/cli/utils.py
@@ -91,6 +91,7 @@ def _get_model_version() -> str:
         ("2", "1"): "v2.1",
         ("2", "2"): "v2.2",
         ("2", "3"): "v2.2",
+        ("2", "4"): "v2.4",
     }
     version_fields = kwja.__version__.split(".")
     return version_map[(version_fields[0], version_fields[1])]
diff --git a/src/kwja/datamodule/datasets/char.py b/src/kwja/datamodule/datasets/char.py
index c23bece7..d17e2905 100644
--- a/src/kwja/datamodule/datasets/char.py
+++ b/src/kwja/datamodule/datasets/char.py
@@ -2,7 +2,6 @@
 from dataclasses import dataclass
 from pathlib import Path
 from typing import Dict, List
-from unicodedata import normalize
 
 from rhoknp import Document
 from transformers import BatchEncoding, PreTrainedTokenizerBase
@@ -14,11 +13,11 @@
     IGNORE_INDEX,
     IGNORE_WORD_NORM_OP_TAG,
     SENT_SEGMENTATION_TAGS,
-    TRANSLATION_TABLE,
     WORD_NORM_OP_TAGS,
     WORD_SEGMENTATION_TAGS,
 )
 from kwja.utils.logging_util import track
+from kwja.utils.normalization import normalize_morpheme
 from kwja.utils.word_normalization import SentenceDenormalizer
 
 logger = logging.getLogger(__name__)
@@ -116,10 +115,9 @@ def _postprocess_document(self, document: Document) -> Document:
                 # e.g. です -> でーす
                 self.denormalizer.denormalize(sentence, self.denormalize_probability)
                 for morpheme in sentence.morphemes:
-                    normalized = normalize("NFKC", morpheme.text).translate(TRANSLATION_TABLE)
-                    if normalized != morpheme.text:
-                        logger.warning(f"apply normalization ({morpheme.text} -> {normalized})")
-                        morpheme.text = normalized
-                        morpheme.lemma = normalize("NFKC", morpheme.lemma).translate(TRANSLATION_TABLE)
+                    surf = morpheme.text
+                    normalize_morpheme(morpheme)
+                    if morpheme.text != surf:
+                        logger.warning(f"apply normalization ({surf} -> {morpheme.text})")
         # propagate updates of morpheme.text to sentence.text and document.text
         return document.reparse()
diff --git a/src/kwja/datamodule/datasets/char_inference.py b/src/kwja/datamodule/datasets/char_inference.py
index 1e7a065c..23c0f6dc 100644
--- a/src/kwja/datamodule/datasets/char_inference.py
+++ b/src/kwja/datamodule/datasets/char_inference.py
@@ -1,4 +1,5 @@
 import logging
+from pathlib import Path
 from typing import Dict, List, Optional
 
 from omegaconf import ListConfig
@@ -8,7 +9,7 @@
 
 from kwja.datamodule.datasets.base import BaseDataset, FullAnnotatedDocumentLoaderMixin
 from kwja.datamodule.datasets.char import CharModuleFeatures
-from kwja.datamodule.datasets.utils import add_doc_ids, add_sent_ids, create_documents_from_raw_texts
+from kwja.datamodule.datasets.utils import add_doc_ids, add_sent_ids, chunk_by_document, create_documents_from_raw_texts
 from kwja.datamodule.examples import CharInferenceExample
 from kwja.utils.logging_util import track
 
@@ -22,10 +23,19 @@ def __init__(
         tokenizer: PreTrainedTokenizerBase,
         max_seq_length: int,
         doc_id_prefix: Optional[str] = None,
+        raw_text_file: Optional[Path] = None,
         **_,
     ) -> None:
         super().__init__(tokenizer, max_seq_length)
-        documents = create_documents_from_raw_texts(texts)
+        documents: List[Document]
+        if len(texts) > 0:
+            documents = create_documents_from_raw_texts(texts)
+        elif raw_text_file is not None:
+            with raw_text_file.open() as f:
+                documents = create_documents_from_raw_texts(chunk_by_document(f))
+        else:
+            documents = []
+
         add_doc_ids(documents, doc_id_prefix)
         documents = self._add_tentative_sentence_boundary(documents)
         super(BaseDataset, self).__init__(documents, tokenizer, max_seq_length, -1)  # document_split_stride must be -1
diff --git a/src/kwja/datamodule/datasets/seq2seq.py b/src/kwja/datamodule/datasets/seq2seq.py
index d53e358a..746cc913 100644
--- a/src/kwja/datamodule/datasets/seq2seq.py
+++ b/src/kwja/datamodule/datasets/seq2seq.py
@@ -10,6 +10,7 @@
 from kwja.datamodule.examples import Seq2SeqExample
 from kwja.utils.constants import IGNORE_INDEX
 from kwja.utils.logging_util import track
+from kwja.utils.normalization import normalize_morpheme
 from kwja.utils.seq2seq_format import Seq2SeqFormatter
 
 logger = logging.getLogger(__name__)
@@ -56,6 +57,7 @@ def _load_examples(self, documents: List[Document], is_train: bool) -> List[Seq2
         examples: List[Seq2SeqExample] = []
         example_id: int = 0
         for document in track(documents, description="Loading examples"):
+            document = self._postprocess_document(document)
             for sentence in document.sentences:
                 src_tokens: List[str] = self.formatter.get_src_tokens(sentence)
                 src_input_ids: List[int] = self.tokenizer.convert_tokens_to_ids(src_tokens) + [
@@ -111,3 +113,12 @@ def encode(self, example: Seq2SeqExample) -> Seq2SeqModuleFeatures:
             attention_mask=example.src_attention_mask,
             seq2seq_labels=seq2seq_labels,
         )
+
+    def _postprocess_document(self, document: Document) -> Document:
+        for morpheme in document.morphemes:
+            surf = morpheme.text
+            normalize_morpheme(morpheme)
+            if morpheme.text != surf:
+                logger.warning(f"apply normalization ({surf} -> {morpheme.text})")
+        # propagate updates of morpheme.text to sentence.text and document.text
+        return document.reparse()
diff --git a/src/kwja/datamodule/datasets/typo.py b/src/kwja/datamodule/datasets/typo.py
index d4c6bf81..5c7c6e02 100644
--- a/src/kwja/datamodule/datasets/typo.py
+++ b/src/kwja/datamodule/datasets/typo.py
@@ -74,7 +74,7 @@ def encode(self, example: TypoExample) -> TypoModuleFeatures:
         )
 
         kdr_labels: List[int] = []
-        for kdr_tag in example.kdrs[:-1]:
+        for kdr_tag in example.kdr_tags[:-1]:
             if kdr_tag in TYPO_CORR_OP_TAG2TOKEN:
                 kdr_label = self.token2token_id[TYPO_CORR_OP_TAG2TOKEN[kdr_tag]]
             else:
@@ -87,7 +87,7 @@ def encode(self, example: TypoExample) -> TypoModuleFeatures:
         kdr_labels += [IGNORE_INDEX] * (self.max_seq_length - len(kdr_labels))
 
         ins_labels: List[int] = []
-        for ins_tag in example.inss:
+        for ins_tag in example.ins_tags:
             if ins_tag in TYPO_CORR_OP_TAG2TOKEN:
                 ins_label = self.token2token_id[TYPO_CORR_OP_TAG2TOKEN[ins_tag]]
             else:
diff --git a/src/kwja/datamodule/datasets/typo_inference.py b/src/kwja/datamodule/datasets/typo_inference.py
index 6e7c081a..11f86679 100644
--- a/src/kwja/datamodule/datasets/typo_inference.py
+++ b/src/kwja/datamodule/datasets/typo_inference.py
@@ -1,5 +1,6 @@
 from collections import defaultdict
-from typing import Dict, List, Tuple
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
 
 from omegaconf import ListConfig
 from rhoknp import Document
@@ -8,7 +9,7 @@
 
 from kwja.datamodule.datasets.base import BaseDataset
 from kwja.datamodule.datasets.typo import TypoModuleFeatures
-from kwja.datamodule.datasets.utils import create_documents_from_raw_texts
+from kwja.datamodule.datasets.utils import chunk_by_document, create_documents_from_raw_texts
 from kwja.datamodule.examples import TypoInferenceExample
 from kwja.utils.constants import DUMMY_TOKEN
 from kwja.utils.logging_util import track
@@ -20,9 +21,17 @@ def __init__(
         texts: ListConfig,
         tokenizer: PreTrainedTokenizerBase,
         max_seq_length: int,
+        raw_text_file: Optional[Path] = None,
     ) -> None:
         super().__init__(tokenizer, max_seq_length)
-        documents: List[Document] = create_documents_from_raw_texts(texts)
+        documents: List[Document]
+        if len(texts) > 0:
+            documents = create_documents_from_raw_texts(texts)
+        elif raw_text_file is not None:
+            with raw_text_file.open() as f:
+                documents = create_documents_from_raw_texts(chunk_by_document(f))
+        else:
+            documents = []
         self.examples: List[TypoInferenceExample] = []
         self.stash: Dict[int, List[Tuple[str, str]]] = defaultdict(list)
         example_id = 0
diff --git a/src/kwja/datamodule/datasets/utils.py b/src/kwja/datamodule/datasets/utils.py
index bd2be401..03568d62 100644
--- a/src/kwja/datamodule/datasets/utils.py
+++ b/src/kwja/datamodule/datasets/utils.py
@@ -1,10 +1,10 @@
 from datetime import datetime
-from typing import List, Optional, Sequence
+from typing import Iterable, Iterator, List, Optional, Sequence, TextIO
 
 from rhoknp import Document
 
 
-def create_documents_from_raw_texts(texts: Sequence[str]) -> List[Document]:
+def create_documents_from_raw_texts(texts: Iterable[str]) -> List[Document]:
     documents: List[Document] = []
     for text in texts:
         raw_text = ""
@@ -37,3 +37,15 @@ def add_sent_ids(documents: Sequence[Document]) -> None:
         for idx, sentence in enumerate(document.sentences):
             if sentence.sent_id == "":
                 sentence.sent_id = f"{document.doc_id}-{idx:0{sent_id_width}}"
+
+
+def chunk_by_document(f: TextIO) -> Iterator[str]:
+    buff: str = ""
+    for line in f:
+        if line.strip() == "EOD":
+            yield buff.rstrip()
+            buff = ""
+        else:
+            buff += line
+    if buff.rstrip() != "":
+        yield buff.rstrip()
diff --git a/src/kwja/datamodule/datasets/word.py b/src/kwja/datamodule/datasets/word.py
index d50814d4..19ad5eec 100644
--- a/src/kwja/datamodule/datasets/word.py
+++ b/src/kwja/datamodule/datasets/word.py
@@ -1,7 +1,7 @@
 import logging
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Dict, List, Literal, Optional, Union
+from typing import Dict, List, Optional, Union
 
 from cohesion_tools.extractors import BridgingExtractor, CoreferenceExtractor, PasExtractor
 from cohesion_tools.extractors.base import BaseExtractor
@@ -25,13 +25,13 @@
     NE_TAGS,
     POS_TAGS,
     RESOURCE_PATH,
-    SPLIT_INTO_WORDS_MODEL_NAMES,
     SUBPOS_TAGS,
     WORD_FEATURES,
     CohesionTask,
 )
 from kwja.utils.kanjidic import KanjiDic
 from kwja.utils.logging_util import track
+from kwja.utils.normalization import normalize_text
 from kwja.utils.reading_prediction import ReadingAligner, get_reading2reading_id
 
 logger = logging.getLogger(__name__)
@@ -78,10 +78,6 @@ def __init__(
     ) -> None:
         super().__init__(tokenizer, max_seq_length)
         self.path = Path(path)
-        if tokenizer.name_or_path in SPLIT_INTO_WORDS_MODEL_NAMES:
-            self.tokenizer_input_format: Literal["words", "text"] = "words"
-        else:
-            self.tokenizer_input_format = "text"
         super(BaseDataset, self).__init__(self.path, tokenizer, max_seq_length, document_split_stride)
         # some tags are not annotated in editorial articles
         self.skip_cohesion_ne_discourse = self.path.parts[-2] == "kyoto_ed"
@@ -89,9 +85,7 @@ def __init__(
         # ---------- reading prediction ----------
         reading_resource_path = RESOURCE_PATH / "reading_prediction"
         self.reading2reading_id = get_reading2reading_id(reading_resource_path / "vocab.txt")
-        self.reading_aligner = ReadingAligner(
-            self.tokenizer, self.tokenizer_input_format, KanjiDic(str(reading_resource_path / "kanjidic"))
-        )
+        self.reading_aligner = ReadingAligner(self.tokenizer, KanjiDic(str(reading_resource_path / "kanjidic")))
 
         # ---------- cohesion analysis ----------
         self.cohesion_tasks: List[CohesionTask] = [task for task in CohesionTask if task.value in cohesion_tasks]
@@ -116,7 +110,7 @@ def __init__(
         self.restrict_cohesion_target: bool = restrict_cohesion_target
 
         # ---------- dependency parsing & cohesion analysis ----------
-        self.special_tokens: List[str] = list(special_tokens)
+        self.special_tokens: List[str] = [st for st in special_tokens if st != " "]
         self.special_encoding: Encoding = self.tokenizer(
             self.special_tokens,
             add_special_tokens=False,
@@ -134,22 +128,18 @@ def __init__(
 
     def _get_tokenized_len(self, document_or_sentence: Union[Document, Sentence]) -> int:
         tokenizer_input: Union[List[str], str] = [m.text for m in document_or_sentence.morphemes]
-        if self.tokenizer_input_format == "text":
-            tokenizer_input = " ".join(tokenizer_input)
-        return len(self.tokenizer.tokenize(tokenizer_input, is_split_into_words=self.tokenizer_input_format == "words"))
+        return len(self.tokenizer.tokenize(tokenizer_input, is_split_into_words=True))
 
     def _load_examples(self, doc_id2document: Dict[str, Document]) -> List[WordExample]:
         examples = []
         example_id = 0
         for document in track(doc_id2document.values(), description="Loading examples"):
-            tokenizer_input: Union[List[str], str] = [m.text for m in document.morphemes]
-            if self.tokenizer_input_format == "text":
-                tokenizer_input = " ".join(tokenizer_input)
+            tokenizer_input: Union[List[str], str] = [normalize_text(m.text) for m in document.morphemes]
             encoding: Encoding = self.tokenizer(
                 tokenizer_input,
                 padding=PaddingStrategy.DO_NOT_PAD,
                 truncation=False,
-                is_split_into_words=self.tokenizer_input_format == "words",
+                is_split_into_words=True,
             ).encodings[0]
             if len(encoding.ids) > self.max_seq_length - len(self.special_tokens):
                 continue
diff --git a/src/kwja/datamodule/datasets/word_inference.py b/src/kwja/datamodule/datasets/word_inference.py
index d22b0251..0992110e 100644
--- a/src/kwja/datamodule/datasets/word_inference.py
+++ b/src/kwja/datamodule/datasets/word_inference.py
@@ -1,6 +1,6 @@
 import logging
 from pathlib import Path
-from typing import Dict, List, Literal, Optional, Union
+from typing import Dict, List, Optional, Union
 
 from cohesion_tools.extractors import BridgingExtractor, CoreferenceExtractor, PasExtractor
 from cohesion_tools.extractors.base import BaseExtractor
@@ -15,7 +15,7 @@
 from kwja.datamodule.datasets.base import BaseDataset, FullAnnotatedDocumentLoaderMixin
 from kwja.datamodule.datasets.word import WordModuleFeatures
 from kwja.datamodule.examples import SpecialTokenIndexer, WordInferenceExample
-from kwja.utils.constants import SPLIT_INTO_WORDS_MODEL_NAMES, CohesionTask
+from kwja.utils.constants import CohesionTask
 from kwja.utils.logging_util import track
 from kwja.utils.sub_document import extract_target_sentences
 
@@ -46,11 +46,6 @@ def __init__(
             # do_predict_after_train
             documents = []
 
-        if tokenizer.name_or_path in SPLIT_INTO_WORDS_MODEL_NAMES:
-            self.tokenizer_input_format: Literal["words", "text"] = "words"
-        else:
-            self.tokenizer_input_format = "text"
-
         super(BaseDataset, self).__init__(documents, tokenizer, max_seq_length, document_split_stride)
         # ---------- cohesion analysis ----------
         self.cohesion_tasks: List[CohesionTask] = [task for task in CohesionTask if task.value in cohesion_tasks]
@@ -75,7 +70,7 @@ def __init__(
         self.restrict_cohesion_target: bool = restrict_cohesion_target
 
         # ---------- dependency parsing & cohesion analysis ----------
-        self.special_tokens: List[str] = list(special_tokens)
+        self.special_tokens: List[str] = [st for st in special_tokens if st != " "]
         self.special_encoding: Encoding = self.tokenizer(
             self.special_tokens,
             add_special_tokens=False,
@@ -88,22 +83,18 @@ def __init__(
 
     def _get_tokenized_len(self, document_or_sentence: Union[Document, Sentence]) -> int:
         tokenizer_input: Union[List[str], str] = [m.text for m in document_or_sentence.morphemes]
-        if self.tokenizer_input_format == "text":
-            tokenizer_input = " ".join(tokenizer_input)
-        return len(self.tokenizer.tokenize(tokenizer_input, is_split_into_words=self.tokenizer_input_format == "words"))
+        return len(self.tokenizer.tokenize(tokenizer_input, is_split_into_words=True))
 
     def _load_examples(self, doc_id2document: Dict[str, Document]) -> List[WordInferenceExample]:
         examples = []
         example_id = 0
         for document in track(doc_id2document.values(), description="Loading examples"):
             tokenizer_input: Union[List[str], str] = [m.text for m in document.morphemes]
-            if self.tokenizer_input_format == "text":
-                tokenizer_input = " ".join(tokenizer_input)
             encoding: Encoding = self.tokenizer(
                 tokenizer_input,
                 padding=PaddingStrategy.DO_NOT_PAD,
                 truncation=False,
-                is_split_into_words=self.tokenizer_input_format == "words",
+                is_split_into_words=True,
             ).encodings[0]
             if len(encoding.ids) > self.max_seq_length - len(self.special_tokens):
                 continue
diff --git a/src/kwja/datamodule/examples/typo.py b/src/kwja/datamodule/examples/typo.py
index 401d63b8..c46b7c8f 100644
--- a/src/kwja/datamodule/examples/typo.py
+++ b/src/kwja/datamodule/examples/typo.py
@@ -8,8 +8,8 @@ class TypoExample:
     doc_id: str
     pre_text: str
     post_text: str
-    kdrs: List[str]
-    inss: List[str]
+    kdr_tags: List[str]
+    ins_tags: List[str]
 
 
 @dataclass(frozen=True)
diff --git a/src/kwja/metrics/char.py b/src/kwja/metrics/char.py
index 209b7eb9..02b57334 100644
--- a/src/kwja/metrics/char.py
+++ b/src/kwja/metrics/char.py
@@ -68,7 +68,10 @@ def _build_documents(self) -> Tuple[List[Document], List[Document], List[Documen
         doc_id2predicted_sentences: Dict[str, List[Sentence]] = defaultdict(list)
         doc_id2partly_gold_sentences: Dict[str, List[Sentence]] = defaultdict(list)
         doc_id2gold_sentences: Dict[str, List[Sentence]] = defaultdict(list)
-        special_ids = set(self.dataset.tokenizer.all_special_ids) - {self.dataset.tokenizer.unk_token_id}
+        special_ids = {
+            getattr(self.dataset.tokenizer, f"{prefix}_token_id")
+            for prefix in ["bos", "eos", "sep", "pad", "cls", "mask"]
+        }
         for (
             example_id,
             sent_segmentation_predictions,
@@ -117,9 +120,11 @@ def _convert_doc_id2sentences_into_documents(
     ) -> List[Document]:
         # Build documents that do not have clauses, phrases, or base phrases, but morphemes only
         return [
-            Document.from_jumanpp("".join(s.to_jumanpp() for s in ss))
-            if from_sentences is False
-            else Document.from_sentences(ss)
+            (
+                Document.from_jumanpp("".join(s.to_jumanpp() for s in ss))
+                if from_sentences is False
+                else Document.from_sentences(ss)
+            )
             for ss in doc_id2sentences.values()
         ]
 
diff --git a/src/kwja/modules/components/logits_processor.py b/src/kwja/modules/components/logits_processor.py
index 2f6cc93e..b9714a5a 100644
--- a/src/kwja/modules/components/logits_processor.py
+++ b/src/kwja/modules/components/logits_processor.py
@@ -1,111 +1,195 @@
 from dataclasses import dataclass
-from typing import Dict, List, Set
+from typing import Dict, List
 
 import regex
 import torch
 from transformers import PreTrainedTokenizerFast
 from transformers.generation import LogitsProcessor
 
+from kwja.modules.functions.loss import mask_logits
 from kwja.utils.constants import (
     CANON_TOKEN,
-    FULL_SPACE_TOKEN,
-    HALF_SPACE_TOKEN1,
-    HALF_SPACE_TOKEN2,
+    HALF_SPACE_TOKEN,
     LEMMA_TOKEN,
+    MORPHEME_DELIMITER_TOKEN,
+    NO_CANON_TOKEN,
     READING_TOKEN,
-    SPECIAL_TO_RARE,
+    SPECIAL2RARE,
     SURF_TOKEN,
-    TRIPLE_DOT_TOKEN,
 )
 
-KANJI_KATAKANA_PATTERN = r"[\p{Script=Han}\p{Script=Katakana}]"
+KANJI_KATAKANA_PAT = r"[\p{Script=Han}\p{Script=Katakana}]"
+
+
+def get_reading_candidate_token_ids(tokenizer: PreTrainedTokenizerFast) -> List[int]:
+    control_tokens = {
+        tokenizer.pad_token,
+        tokenizer.eos_token,
+        SURF_TOKEN,
+        READING_TOKEN,
+        LEMMA_TOKEN,
+        CANON_TOKEN,
+        NO_CANON_TOKEN,
+        MORPHEME_DELIMITER_TOKEN,
+    }
+    return [
+        token_id
+        for token, token_id in tokenizer.vocab.items()
+        # 漢字またはカタカナを含むトークンIDは除外（読みには漢字とカタカナが含まれないので）
+        if regex.search(KANJI_KATAKANA_PAT, token) is None and token not in control_tokens
+    ]
+
+
+def get_char2token_items(tokenizer: PreTrainedTokenizerFast) -> Dict[str, Dict[str, int]]:
+    char2token_items: Dict[str, Dict[str, int]] = {}
+    for token, token_id in tokenizer.vocab.items():
+        if token.startswith("▁"):
+            if len(token) == 1:
+                continue
+            char: str = token[1]
+        else:
+            char = token[0]
+        char2token_items.setdefault(char, {})
+        char2token_items[char][token] = token_id
+    return char2token_items
 
 
-@dataclass()
-class TargetMorpheme:
+@dataclass
+class TargetProperty:
     surf: bool = False
     reading: bool = False
     lemma: bool = False
     canon: bool = False
 
 
-def get_reading_candidates(tokenizer: PreTrainedTokenizerFast) -> Set[int]:
-    candidates: Set[int] = set()
-    for token, vocab_id in tokenizer.vocab.items():
-        if not bool(regex.search(KANJI_KATAKANA_PATTERN, token)):
-            # 漢字またはカタカナを含む語彙は読みからは除外（＝読みには漢字とカタカナが含まれない）
-            candidates.add(vocab_id)
-    return candidates
-
-
-def get_char2tokens(tokenizer: PreTrainedTokenizerFast) -> Dict[str, Dict[str, int]]:
-    char2tokens: Dict[str, Dict[str, int]] = {}
-    for vocab_token, vocab_id in tokenizer.get_vocab().items():
-        if vocab_token.startswith("▁"):
-            if len(vocab_token) == 1:
-                continue
-            char: str = vocab_token[1]
-        else:
-            char = vocab_token[0]
-        if char not in char2tokens:
-            char2tokens[char] = {}
-        char2tokens[char][vocab_token] = vocab_id
-    return char2tokens
-
-
-class ForcedLogitsProcessor(LogitsProcessor):
+class SurfForcedDecodingLogitsProcessor(LogitsProcessor):
     def __init__(
         self,
-        surfs: List[List[str]],
+        batch_surfs: List[List[str]],
         num_beams: int,
         tokenizer: PreTrainedTokenizerFast,
-        reading_candidates: Set[int],
-        char2tokens: Dict[str, Dict[str, int]],
+        char2token_items: Dict[str, Dict[str, int]],
+        reading_candidate_token_ids: List[int],
     ) -> None:
-        self.tokenizer = tokenizer
-        self.surfs: List[List[str]] = surfs
+        self.batch_surfs: List[List[str]] = batch_surfs
         self.num_beams: int = num_beams
-        self.char2tokens: Dict[str, Dict[str, int]] = char2tokens
-        self.eos_token_id: int = self.tokenizer.eos_token_id
-
-        self.surf_token_id: int = tokenizer.convert_tokens_to_ids(SURF_TOKEN)
-        self.reading_token_id: int = tokenizer.convert_tokens_to_ids(READING_TOKEN)
-        self.lemma_token_id: int = tokenizer.convert_tokens_to_ids(LEMMA_TOKEN)
-        self.canon_token_id: int = tokenizer.convert_tokens_to_ids(CANON_TOKEN)
-
-        self.ids_except_surf: List[int] = list(set(self.tokenizer.get_vocab().values()) - {self.surf_token_id})
-        self.ids_except_reading: Set[int] = set(self.tokenizer.get_vocab().values()) - {self.reading_token_id}
-        self.ids_except_kanji_and_katakana: Set[int] = set(self.tokenizer.get_vocab().values()) - reading_candidates
-
-        self.token_to_ids_except_token: Dict[str, Set[int]] = {}
-        special_tokens: List[str] = [FULL_SPACE_TOKEN, HALF_SPACE_TOKEN1, HALF_SPACE_TOKEN2, TRIPLE_DOT_TOKEN] + list(
-            SPECIAL_TO_RARE.keys()
-        )
-        for special_token in special_tokens:
-            self.token_to_ids_except_token[special_token] = set(self.tokenizer.get_vocab().values()) - {
-                self.tokenizer.convert_tokens_to_ids(special_token)
-            }
-        self.is_finished: List[bool] = [False] * len(self.surfs)
-
-    def _get_target_morpheme(self, input_ids: List[int]) -> TargetMorpheme:
-        target_morpheme: TargetMorpheme = TargetMorpheme()
-        for input_id in input_ids[::-1]:
-            if input_id == self.surf_token_id:
-                target_morpheme.surf = True
+
+        self.tokenizer = tokenizer
+        self.vocab = tokenizer.vocab  # cache
+
+        self.char2token_items: Dict[str, Dict[str, int]] = char2token_items
+        self.reading_candidate_token_ids: List[int] = reading_candidate_token_ids
+
+        self.is_finished: List[bool] = [False] * len(batch_surfs)
+
+    def __call__(self, batch_prev_input_ids: torch.Tensor, batch_logits: torch.Tensor) -> torch.Tensor:
+        # Falseならば-inf（-infでないと対応するトークンが選ばれてしまう可能性がある）
+        mask = self.get_mask(batch_prev_input_ids, batch_logits)
+        batch_masked_logits = mask_logits(batch_logits, mask, mask_value=-float("inf"))
+        return batch_masked_logits
+
+    def get_mask(
+        self,
+        batch_prev_input_ids: torch.Tensor,  # (b * num_beams, seq_len)
+        batch_logits: torch.Tensor,  # (b * num_beams, vocab_size)
+    ) -> torch.Tensor:
+        _, seq_len = batch_prev_input_ids.size()
+        if seq_len == 1:
+            mask = torch.zeros_like(batch_logits, dtype=torch.bool)
+            mask[:, self.vocab[SURF_TOKEN]] = True
+            return mask
+        else:
+            batch_masks = []
+            for i, (prev_input_ids, logits) in enumerate(zip(batch_prev_input_ids.tolist(), batch_logits)):
+                batch_idx = i // self.num_beams
+                if self.is_finished[batch_idx]:
+                    mask = torch.zeros_like(logits, dtype=torch.bool)
+                    mask[self.tokenizer.eos_token_id] = True
+                    batch_masks.append(mask)
+                    continue
+                target_property: TargetProperty = self._get_target_property(prev_input_ids)
+                if target_property.surf is True:
+                    batch_masks.append(self._get_surf_mask(prev_input_ids, logits, batch_idx))
+                elif target_property.reading is True:
+                    batch_masks.append(self._get_reading_mask(prev_input_ids, logits))
+                elif target_property.lemma is True:
+                    batch_masks.append(self._get_lemma_mask(prev_input_ids, logits))
+                elif target_property.canon is True:
+                    batch_masks.append(self._get_canon_mask(prev_input_ids, logits, batch_idx))
+            return torch.stack(batch_masks)
+
+    def _get_target_property(self, prev_input_ids: List[int]) -> TargetProperty:
+        target_property = TargetProperty()
+        for prev_input_id in prev_input_ids[::-1]:
+            if prev_input_id == self.vocab[SURF_TOKEN]:
+                target_property.surf = True
                 break
-            elif input_id == self.reading_token_id:
-                target_morpheme.reading = True
+            elif prev_input_id == self.vocab[READING_TOKEN]:
+                target_property.reading = True
                 break
-            elif input_id == self.lemma_token_id:
-                target_morpheme.lemma = True
+            elif prev_input_id == self.vocab[LEMMA_TOKEN]:
+                target_property.lemma = True
                 break
-            elif input_id == self.canon_token_id:
-                target_morpheme.canon = True
+            elif prev_input_id == self.vocab[CANON_TOKEN]:
+                target_property.canon = True
                 break
-        return target_morpheme
+        return target_property
+
+    def _get_surf_mask(self, prev_input_ids: List[int], logits: torch.Tensor, batch_idx: int) -> torch.Tensor:
+        surf_mask = torch.zeros_like(logits, dtype=torch.bool)
+        if ungenerated_surf := self._get_ungenerated_surf(prev_input_ids, self.batch_surfs[batch_idx]):
+            surf_mask[self._get_permitted_token_ids(ungenerated_surf)] = True
+        else:
+            surf_mask[self.vocab[READING_TOKEN]] = True
+        return surf_mask
 
-    def _get_remaining_surf(self, input_ids: List[int], surf: List[str]) -> str:
-        decoded: str = self.tokenizer.decode(input_ids)
+    def _get_reading_mask(self, prev_input_ids: List[int], logits: torch.Tensor) -> torch.Tensor:
+        reading_mask = torch.zeros_like(logits, dtype=torch.bool)
+        if prev_input_ids[-1] == self.vocab[READING_TOKEN]:
+            reading_mask[self.reading_candidate_token_ids] = True
+        else:
+            reading_mask[self.reading_candidate_token_ids + [self.vocab[LEMMA_TOKEN]]] = True
+        return reading_mask
+
+    def _get_lemma_mask(self, prev_input_ids: List[int], logits: torch.Tensor) -> torch.Tensor:
+        lemma_mask = torch.ones_like(logits, dtype=torch.bool)
+        prohibited_token_ids = [
+            self.tokenizer.pad_token_id,
+            self.tokenizer.eos_token_id,
+            self.vocab[SURF_TOKEN],
+            self.vocab[READING_TOKEN],
+            self.vocab[LEMMA_TOKEN],
+            self.vocab[NO_CANON_TOKEN],
+            self.vocab[MORPHEME_DELIMITER_TOKEN],
+        ]
+        if prev_input_ids[-1] == self.vocab[LEMMA_TOKEN]:
+            prohibited_token_ids.append(self.vocab[CANON_TOKEN])
+        lemma_mask[prohibited_token_ids] = False
+        return lemma_mask
+
+    def _get_canon_mask(self, prev_input_ids: List[int], logits: torch.Tensor, batch_idx: int) -> torch.Tensor:
+        canon_mask = torch.ones_like(logits, dtype=torch.bool)
+        prohibited_token_ids = [
+            self.tokenizer.pad_token_id,
+            self.vocab[READING_TOKEN],
+            self.vocab[LEMMA_TOKEN],
+            self.vocab[CANON_TOKEN],
+            self.vocab[MORPHEME_DELIMITER_TOKEN],
+        ]
+        if prev_input_ids[-1] == self.vocab[CANON_TOKEN]:
+            prohibited_token_ids += [self.tokenizer.eos_token_id, self.vocab[SURF_TOKEN]]
+        else:
+            if prev_input_ids.count(self.vocab[READING_TOKEN]) < len(self.batch_surfs[batch_idx]):
+                prohibited_token_ids += [self.tokenizer.eos_token_id, self.vocab[NO_CANON_TOKEN]]
+            else:
+                prohibited_token_ids += [self.vocab[SURF_TOKEN], self.vocab[NO_CANON_TOKEN]]
+                if logits.argmax().item() == self.tokenizer.eos_token_id:
+                    self.is_finished[batch_idx] = True
+        canon_mask[prohibited_token_ids] = False
+        return canon_mask
+
+    def _get_ungenerated_surf(self, prev_input_ids: List[int], surfs: List[str]) -> str:
+        decoded: str = self.tokenizer.decode(prev_input_ids)
         surf_index: int = 0
         generated_surf: str = ""
         for line in decoded.split(SURF_TOKEN):
@@ -113,69 +197,15 @@ def _get_remaining_surf(self, input_ids: List[int], surf: List[str]) -> str:
                 surf_index += 1
             else:
                 generated_surf = line.strip(" ")
-        return surf[surf_index][len(generated_surf) :]
-
-    def _get_banned_token_ids(self, text: str) -> Set[int]:
-        for token, ids_except_token in self.token_to_ids_except_token.items():
-            if text.startswith(token):
-                return ids_except_token
-        permitted_token_ids: Set[int] = set()
-        for vocab_token, vocab_id in self.char2tokens[text[0]].items():
-            if (vocab_token.startswith("▁") and text.startswith(vocab_token[1:])) or text.startswith(vocab_token):
-                permitted_token_ids.add(vocab_id)
-        return set(self.tokenizer.get_vocab().values()) - permitted_token_ids
-
-    def _get_generated_surf(self, input_ids: List[int]) -> List[str]:
-        decoded: str = self.tokenizer.decode(input_ids)
-        generated_surf: List[str] = []
-        for line in decoded.split(SURF_TOKEN)[1:]:
-            generated_surf.append(line.split(READING_TOKEN)[0].strip(" "))
-        return generated_surf
-
-    def get_mask(self, prev_input_ids: torch.Tensor, scores: torch.Tensor) -> torch.Tensor:
-        mask: torch.Tensor = torch.zeros_like(scores).bool()
-        _, seq_len = prev_input_ids.size()
-        if seq_len == 1:
-            mask[:, self.ids_except_surf] = True
-            return mask
-
-        for hypo_idx, input_ids in enumerate(prev_input_ids.tolist()):
-            if self.is_finished[hypo_idx // self.num_beams]:
-                continue
-            target_morpheme: TargetMorpheme = self._get_target_morpheme(input_ids)
-
-            banned_token_ids: Set[int] = {
-                self.eos_token_id,
-                self.surf_token_id,
-                self.reading_token_id,
-                self.lemma_token_id,
-                self.canon_token_id,
-            }
-            if target_morpheme.surf:
-                if remaining_surf := self._get_remaining_surf(input_ids, self.surfs[hypo_idx // self.num_beams]):
-                    banned_token_ids |= self._get_banned_token_ids(remaining_surf)
-                else:
-                    banned_token_ids = self.ids_except_reading
-            elif target_morpheme.reading:
-                banned_token_ids |= self.ids_except_kanji_and_katakana
-                if input_ids[-1] != self.reading_token_id:
-                    banned_token_ids.discard(self.lemma_token_id)
-            elif target_morpheme.lemma:
-                if input_ids[-1] != self.lemma_token_id:
-                    banned_token_ids.discard(self.canon_token_id)
-            elif target_morpheme.canon:
-                if input_ids[-1] != self.canon_token_id:
-                    generated_surf: List[str] = self._get_generated_surf(input_ids)
-                    if len(generated_surf) == len(self.surfs[hypo_idx // self.num_beams]):
-                        banned_token_ids.discard(self.eos_token_id)
-                        self.is_finished[hypo_idx // self.num_beams] = True
-                    else:
-                        banned_token_ids.discard(self.surf_token_id)
-            mask[hypo_idx, list(banned_token_ids)] = True
-        return mask
-
-    def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor) -> torch.Tensor:
-        input_ids[input_ids == -100] = self.tokenizer.pad_token_id
-        mask: torch.Tensor = self.get_mask(input_ids, scores)
-        scores.masked_fill_(mask, -float("inf"))
-        return scores
+        return surfs[surf_index][len(generated_surf) :]
+
+    def _get_permitted_token_ids(self, ungenerated_surf: str) -> List[int]:
+        for special_token in [HALF_SPACE_TOKEN] + list(SPECIAL2RARE.keys()):
+            if ungenerated_surf.startswith(special_token):
+                return [self.vocab[special_token]]
+
+        permitted_token_ids: List[int] = []
+        for token, token_id in self.char2token_items[ungenerated_surf[0]].items():
+            if ungenerated_surf.startswith(token) or (token.startswith("▁") and ungenerated_surf.startswith(token[1:])):
+                permitted_token_ids.append(token_id)
+        return permitted_token_ids
diff --git a/src/kwja/modules/functions/loss.py b/src/kwja/modules/functions/loss.py
index 893301c7..4bc1bf94 100644
--- a/src/kwja/modules/functions/loss.py
+++ b/src/kwja/modules/functions/loss.py
@@ -50,5 +50,5 @@ def compute_cohesion_analysis_loss(
     return _average_loss(-log_softmax, target)
 
 
-def mask_logits(logits: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
-    return torch.where(mask, logits, torch.full_like(logits, MASKED))
+def mask_logits(logits: torch.Tensor, mask: torch.Tensor, mask_value: float = MASKED) -> torch.Tensor:
+    return torch.where(mask, logits, torch.full_like(logits, mask_value))
diff --git a/src/kwja/modules/seq2seq.py b/src/kwja/modules/seq2seq.py
index 9f90909c..3b3bb1f8 100644
--- a/src/kwja/modules/seq2seq.py
+++ b/src/kwja/modules/seq2seq.py
@@ -9,7 +9,11 @@
 from transformers.generation import LogitsProcessorList
 
 from kwja.modules.base import BaseModule
-from kwja.modules.components.logits_processor import ForcedLogitsProcessor, get_char2tokens, get_reading_candidates
+from kwja.modules.components.logits_processor import (
+    SurfForcedDecodingLogitsProcessor,
+    get_char2token_items,
+    get_reading_candidate_token_ids,
+)
 
 if os.environ.get("KWJA_CLI_MODE") == "1":
     from kwja.modules.base import DummyModuleMetric as Seq2SeqModuleMetric  # dummy class for faster loading
@@ -25,17 +29,17 @@ def __init__(self, hparams: DictConfig) -> None:
 
         self.encoder_decoder: PreTrainedModel = hydra.utils.call(hparams.encoder.from_config)
         # https://github.com/huggingface/transformers/issues/4875
-        self.encoder_decoder.resize_token_embeddings(len(self.tokenizer.get_vocab()))
+        self.encoder_decoder.resize_token_embeddings(len(self.tokenizer.vocab))
 
-        self.reading_candidates = get_reading_candidates(self.tokenizer)
-        self.char2tokens = get_char2tokens(self.tokenizer)
-        self.use_forced_decoding: bool = getattr(hparams, "use_forced_decoding", False)
+        self.reading_candidate_token_ids = get_reading_candidate_token_ids(self.tokenizer)
+        self.char2token_items = get_char2token_items(self.tokenizer)
+        self.use_surf_forced_decoding: bool = getattr(hparams, "use_surf_forced_decoding", False)
 
     def setup(self, stage: str) -> None:
         if stage == "fit":
             self.encoder_decoder = hydra.utils.call(self.hparams.encoder.from_pretrained)
             # https://github.com/huggingface/transformers/issues/4875
-            self.encoder_decoder.resize_token_embeddings(len(self.tokenizer.get_vocab()))
+            self.encoder_decoder.resize_token_embeddings(len(self.tokenizer.vocab))
 
     def forward(self, batch: Any) -> Dict[str, torch.Tensor]:
         output = self.encoder_decoder(
@@ -88,19 +92,21 @@ def predict_step(self, batch: Any) -> Dict[str, Any]:
         generations = self.encoder_decoder.generate(
             input_ids=batch["input_ids"],
             attention_mask=batch["attention_mask"],
-            logits_processor=LogitsProcessorList(
-                [
-                    ForcedLogitsProcessor(
-                        surfs=batch["surfs"],
-                        num_beams=self.hparams.decoding.num_beams,
-                        tokenizer=self.tokenizer,
-                        reading_candidates=self.reading_candidates,
-                        char2tokens=self.char2tokens,
-                    ),
-                ]
-            )
-            if self.use_forced_decoding
-            else None,
+            logits_processor=(
+                LogitsProcessorList(
+                    [
+                        SurfForcedDecodingLogitsProcessor(
+                            batch_surfs=batch["surfs"],
+                            num_beams=self.hparams.decoding.num_beams,
+                            tokenizer=self.tokenizer,
+                            char2token_items=self.char2token_items,
+                            reading_candidate_token_ids=self.reading_candidate_token_ids,
+                        ),
+                    ]
+                )
+                if self.use_surf_forced_decoding
+                else None
+            ),
             **self.hparams.decoding,
         )
         if isinstance(generations, torch.Tensor):
diff --git a/src/kwja/utils/constants.py b/src/kwja/utils/constants.py
index 1f1b8dc6..7748d197 100644
--- a/src/kwja/utils/constants.py
+++ b/src/kwja/utils/constants.py
@@ -16,14 +16,6 @@
 RESOURCE_PATH = resource_files(kwja) / "resource"
 
 
-# ---------- word (inference) dataset ----------
-SPLIT_INTO_WORDS_MODEL_NAMES = [
-    "nlp-waseda/roberta-base-japanese",
-    "nlp-waseda/roberta-large-japanese",
-    "nlp-waseda/roberta-large-japanese-seq512",
-]
-
-
 # ---------- typo module ----------
 TYPO_CORR_OP_TAG2TOKEN = {
     "K": "<k>",
@@ -205,47 +197,41 @@
 # ---------- char module|text normalization ----------
 # 制御文字(\t,\nを含む)は削除
 TRANSLATION_TABLE: Dict[int, Optional[int]] = str.maketrans(
-    ' "#▁', "␣”＃▂", "".join(chr(i) for i in [*range(32), *range(127, 160)])
+    '"#▁', "”＃▂", "".join(chr(i) for i in [*range(32), *range(127, 160), 5760, *range(8232, 8234)])
 )
 
 
 # ---------- seq2seq module----------
-# tokens to separate surface, reading, lemma, and canonical form
 SURF_TOKEN: str = "<extra_id_0>"
 READING_TOKEN: str = "<extra_id_1>"
 LEMMA_TOKEN: str = "<extra_id_2>"
 CANON_TOKEN: str = "<extra_id_3>"
-# tokens to represent full space, half space, no canonical form, and triple dots
-NO_CANON_TOKEN: str = "<extra_id_4>"
-FULL_SPACE_TOKEN: str = "<extra_id_5>"
-HALF_SPACE_TOKEN1: str = "<extra_id_6>"
-HALF_SPACE_TOKEN2: str = "<extra_id_7>"
-TRIPLE_DOT_TOKEN: str = "<extra_id_8>"
-# token to split input text into morphemes
-MORPHEME_SPLIT_TOKEN: str = "<extra_id_9>"
-# tokens for unk tokens
-RARE_TO_SPECIAL: Dict[str, str] = {
-    "ゔ": "<extra_id_10>",
-    "榕": "<extra_id_11>",
-    "謄": "<extra_id_12>",
-    "丿": "<extra_id_13>",
-    "孜": "<extra_id_14>",
-    "腑": "<extra_id_15>",
-    "庖": "<extra_id_16>",
-    "┘": "<extra_id_17>",
-    "秧": "<extra_id_18>",
-    "褪": "<extra_id_19>",
-    "疥": "<extra_id_20>",
-    "鮪": "<extra_id_21>",
-    "髑髏": "<extra_id_22>",
-    "侭": "<extra_id_23>",
-    "蒟蒻": "<extra_id_24>",
-    "╹": "<extra_id_25>",
-    "厂": "<extra_id_26>",
-    "Ӧ": "<extra_id_27>",
-    "溢": "<extra_id_28>",
+NO_CANON_TOKEN: str = "<extra_id_4>"  # control token to represent no canonical form
+MORPHEME_DELIMITER_TOKEN: str = "<extra_id_5>"  # control token to segment input text into morphemes
+# special tokens
+HALF_SPACE_TOKEN: str = "<extra_id_6>"
+RARE2SPECIAL: Dict[str, str] = {
+    "ゔ": "<extra_id_7>",
+    "榕": "<extra_id_8>",
+    "謄": "<extra_id_9>",
+    "丿": "<extra_id_10>",
+    "孜": "<extra_id_11>",
+    "腑": "<extra_id_12>",
+    "庖": "<extra_id_13>",
+    "┘": "<extra_id_14>",
+    "秧": "<extra_id_15>",
+    "褪": "<extra_id_16>",
+    "疥": "<extra_id_17>",
+    "鮪": "<extra_id_18>",
+    "髑髏": "<extra_id_19>",
+    "侭": "<extra_id_20>",
+    "蒟蒻": "<extra_id_21>",
+    "╹": "<extra_id_22>",
+    "厂": "<extra_id_23>",
+    "Ӧ": "<extra_id_24>",
+    "溢": "<extra_id_25>",
 }
-SPECIAL_TO_RARE: Dict[str, str] = {v: k for k, v in RARE_TO_SPECIAL.items()}
+SPECIAL2RARE: Dict[str, str] = {v: k for k, v in RARE2SPECIAL.items()}
 
 
 # ---------- word module ----------
@@ -1054,7 +1040,15 @@ class CohesionTask(Enum):
         "ヤ列基本推量形": 11,
         "ヤ列基本省略推量形": 12,
     },
-    "助動詞そうだ型": {"*": 0, "語幹": 1, "基本形": 2, "ダ列タ系連用テ形": 3, "デアル列基本形": 4, "デス列基本形": 5, "デス列音便基本形": 6},
+    "助動詞そうだ型": {
+        "*": 0,
+        "語幹": 1,
+        "基本形": 2,
+        "ダ列タ系連用テ形": 3,
+        "デアル列基本形": 4,
+        "デス列基本形": 5,
+        "デス列音便基本形": 6,
+    },
     "助動詞く型": {"*": 0, "語幹": 1, "基本形": 2, "基本連用形": 3, "文語連体形": 4, "文語未然形": 5},
     "動詞性接尾辞ます型": {
         "*": 0,
diff --git a/src/kwja/utils/normalization.py b/src/kwja/utils/normalization.py
new file mode 100644
index 00000000..a45dbde4
--- /dev/null
+++ b/src/kwja/utils/normalization.py
@@ -0,0 +1,18 @@
+from unicodedata import normalize
+
+from rhoknp import Morpheme
+
+from kwja.utils.constants import TRANSLATION_TABLE
+
+
+def normalize_text(text: str) -> str:
+    return normalize("NFKC", text).translate(TRANSLATION_TABLE)
+
+
+def normalize_morpheme(morpheme: Morpheme) -> None:
+    morpheme.text = normalize_text(morpheme.text)
+    morpheme.reading = normalize_text(morpheme.reading.replace("う゛", "ゔ"))
+    morpheme.lemma = normalize_text(morpheme.lemma)
+    canon = morpheme.semantics.get("代表表記")
+    if isinstance(canon, str):
+        morpheme.semantics["代表表記"] = normalize_text(canon)
diff --git a/src/kwja/utils/reading_prediction.py b/src/kwja/utils/reading_prediction.py
index c4369f58..31470e90 100644
--- a/src/kwja/utils/reading_prediction.py
+++ b/src/kwja/utils/reading_prediction.py
@@ -5,11 +5,11 @@
 from collections import defaultdict
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Dict, List, Literal, Optional, Tuple, Union
+from typing import Dict, List, Optional, Tuple, Union
 
 import numpy as np
-from rhoknp import Document, Morpheme
-from transformers import AutoTokenizer, PreTrainedTokenizerBase
+from rhoknp import Morpheme
+from transformers import PreTrainedTokenizerBase
 
 from kwja.utils.constants import (
     CHOON_SET,
@@ -42,21 +42,14 @@ def get_reading2reading_id(path: Path) -> Dict[str, int]:
 class ReadingAligner:
     kana_re = re.compile("^[\u3041-\u30FF]+$")
 
-    def __init__(
-        self, tokenizer: PreTrainedTokenizerBase, tokenizer_input_format: Literal["words", "text"], kanji_dic: KanjiDic
-    ) -> None:
+    def __init__(self, tokenizer: PreTrainedTokenizerBase, kanji_dic: KanjiDic) -> None:
         self.tokenizer = tokenizer
-        self.tokenizer_input_format = tokenizer_input_format
         self.kanji_dic = kanji_dic
 
     def align(self, morphemes: List[Morpheme]) -> List[str]:
         # assumption: morphemes are never combined
         tokenizer_input: Union[List[str], str] = [m.text for m in morphemes]
-        if self.tokenizer_input_format == "text":
-            tokenizer_input = " ".join(tokenizer_input)
-        encoding = self.tokenizer(
-            tokenizer_input, add_special_tokens=False, is_split_into_words=self.tokenizer_input_format == "words"
-        ).encodings[0]
+        encoding = self.tokenizer(tokenizer_input, add_special_tokens=False, is_split_into_words=True).encodings[0]
         word_id2subwords = defaultdict(list)
         for token_id, word_id in enumerate(encoding.word_ids):
             word_id2subwords[word_id].append(self.tokenizer.decode(encoding.ids[token_id]))
@@ -293,46 +286,5 @@ def get_word_level_readings(readings: List[str], tokens: List[str], subword_map:
         if item:
             ret.append(item)
         elif any(flags):
-            ret.append("␣")
+            ret.append("_")
     return ret
-
-
-def main():
-    from argparse import ArgumentParser
-    from collections import Counter
-    from pathlib import Path
-
-    from kwja.utils.constants import SPLIT_INTO_WORDS_MODEL_NAMES
-
-    parser = ArgumentParser()
-    parser.add_argument("-m", "--model-name-or-path", type=str, help="model_name_or_path")
-    parser.add_argument("-k", "--kanjidic", type=str, help="path to file")
-    parser.add_argument("-i", "--input", type=str, help="path to input dir")
-    args = parser.parse_args()
-
-    tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
-    if args.model_name_or_path in SPLIT_INTO_WORDS_MODEL_NAMES:
-        tokenizer_input_format: Literal["words", "text"] = "words"
-    else:
-        tokenizer_input_format = "text"
-    kanjidic = KanjiDic(args.kanjidic)
-    reading_aligner = ReadingAligner(tokenizer, tokenizer_input_format, kanjidic)
-
-    reading_counter: Dict[str, int] = Counter()
-    for path in Path(args.input).glob("**/*.knp"):
-        logger.info(f"processing {path}")
-        with path.open() as f:
-            document = Document.from_knp(f.read())
-        try:
-            for reading in reading_aligner.align(document.morphemes):
-                reading_counter[reading] += 1
-        except ValueError:
-            logger.warning(f"skip {document.doc_id} for an error")
-    for subreading, count in sorted(
-        sorted(reading_counter.items(), key=lambda pair: pair[0]), key=lambda pair: pair[1], reverse=True
-    ):
-        print(f"{subreading}\t{count}")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/src/kwja/utils/seq2seq_format.py b/src/kwja/utils/seq2seq_format.py
index a00a4d27..71c3047c 100644
--- a/src/kwja/utils/seq2seq_format.py
+++ b/src/kwja/utils/seq2seq_format.py
@@ -5,17 +5,14 @@
 
 from kwja.utils.constants import (
     CANON_TOKEN,
-    FULL_SPACE_TOKEN,
-    HALF_SPACE_TOKEN1,
-    HALF_SPACE_TOKEN2,
+    HALF_SPACE_TOKEN,
     LEMMA_TOKEN,
-    MORPHEME_SPLIT_TOKEN,
+    MORPHEME_DELIMITER_TOKEN,
     NO_CANON_TOKEN,
-    RARE_TO_SPECIAL,
+    RARE2SPECIAL,
     READING_TOKEN,
-    SPECIAL_TO_RARE,
+    SPECIAL2RARE,
     SURF_TOKEN,
-    TRIPLE_DOT_TOKEN,
 )
 
 
@@ -23,68 +20,51 @@ class Seq2SeqFormatter:
     def __init__(self, tokenizer: PreTrainedTokenizerFast) -> None:
         self.tokenizer: PreTrainedTokenizerFast = tokenizer
 
-        self.word_to_token: Dict[str, str] = {
-            "\u3000": FULL_SPACE_TOKEN,
-            " ": HALF_SPACE_TOKEN1,
-            "␣": HALF_SPACE_TOKEN2,
-            "…": TRIPLE_DOT_TOKEN,
-        }
-        self.token_to_word: Dict[str, str] = {v: k for k, v in self.word_to_token.items()}
+        self.word2token: Dict[str, str] = {" ": HALF_SPACE_TOKEN}
+        self.token2word: Dict[str, str] = {v: k for k, v in self.word2token.items()}
 
     def get_surfs(self, sentence: Sentence) -> List[str]:
         surfs: List[str] = []
         for morpheme in sentence.morphemes:
             surf: str = morpheme.surf
-            for k, v in self.word_to_token.items():
-                surf = surf.replace(k, v)
-            surf = surf.replace(HALF_SPACE_TOKEN2, HALF_SPACE_TOKEN1)
-            for k, v in RARE_TO_SPECIAL.items():
+            for word, token in self.word2token.items():
+                surf = surf.replace(word, token)
+            for k, v in RARE2SPECIAL.items():
                 surf = surf.replace(k, v)
             tokenized_surf: List[str] = [x for x in self.tokenizer.tokenize(surf) if x != "▁"]
             decoded: str = self.tokenizer.decode(self.tokenizer.convert_tokens_to_ids(tokenized_surf))
-            for token in self.word_to_token.values():
+            for token in self.word2token.values():
                 decoded = decoded.replace(f"{token} ", token)
-            for token in SPECIAL_TO_RARE:
+            for token in SPECIAL2RARE:
                 decoded = decoded.replace(f"{token} ", token)
-            surfs.append(decoded.replace(" ", HALF_SPACE_TOKEN1))
+            surfs.append(decoded.replace(" ", HALF_SPACE_TOKEN))
         return surfs
 
     def get_src_tokens(self, sentence: Sentence) -> List[str]:
-        src_text: str = MORPHEME_SPLIT_TOKEN.join(m.surf for m in sentence.morphemes)
-        for k, v in self.word_to_token.items():
-            src_text = src_text.replace(k, v)
-        src_text = src_text.replace(HALF_SPACE_TOKEN2, HALF_SPACE_TOKEN1)
-        for k, v in RARE_TO_SPECIAL.items():
-            src_text = src_text.replace(k, v)
+        src_text: str = MORPHEME_DELIMITER_TOKEN.join(m.surf for m in sentence.morphemes)
+        for word, token in self.word2token.items():
+            src_text = src_text.replace(word, token)
+        for rare, special in RARE2SPECIAL.items():
+            src_text = src_text.replace(rare, special)
         return [x for x in self.tokenizer.tokenize(src_text) if x != "▁"]
 
     def get_tgt_tokens(self, sentence: Sentence) -> List[str]:
         seq2seq_format: str = ""
         for morpheme in sentence.morphemes:
-            if morpheme.surf == "\u3000":
-                surf: str = FULL_SPACE_TOKEN
-                reading: str = FULL_SPACE_TOKEN
-                lemma: str = FULL_SPACE_TOKEN
-                canon: str = "/"
-            elif morpheme.surf == " ":
-                surf = HALF_SPACE_TOKEN1
-                reading = HALF_SPACE_TOKEN1
-                lemma = HALF_SPACE_TOKEN1
+            if morpheme.surf == " ":
+                surf = HALF_SPACE_TOKEN
+                reading = HALF_SPACE_TOKEN
+                lemma = HALF_SPACE_TOKEN
                 canon = "/"
-            elif morpheme.surf == "…":
-                surf = TRIPLE_DOT_TOKEN
-                reading = TRIPLE_DOT_TOKEN
-                lemma = TRIPLE_DOT_TOKEN
-                canon = f"{TRIPLE_DOT_TOKEN}/{TRIPLE_DOT_TOKEN}"
             else:
                 surf = morpheme.surf
-                if morpheme.reading == "\u3000":
-                    reading = FULL_SPACE_TOKEN
+                if morpheme.reading == " ":  # 読みがない場合
+                    reading = HALF_SPACE_TOKEN
                 elif "/" in morpheme.reading and len(morpheme.reading) > 1:
                     reading = morpheme.reading.split("/")[0]
                 else:
                     reading = morpheme.reading
-                lemma = FULL_SPACE_TOKEN if morpheme.lemma == "\u3000" else morpheme.lemma
+                lemma = morpheme.lemma
                 if morpheme.canon is not None:
                     canon = morpheme.canon
                     canon_list: List[str] = canon.split("/")
@@ -93,7 +73,7 @@ def get_tgt_tokens(self, sentence: Sentence) -> List[str]:
                 else:
                     canon = NO_CANON_TOKEN
             seq2seq_format += f"{SURF_TOKEN}{surf}{READING_TOKEN}{reading}{LEMMA_TOKEN}{lemma}{CANON_TOKEN}{canon}"
-        for k, v in RARE_TO_SPECIAL.items():
+        for k, v in RARE2SPECIAL.items():
             seq2seq_format = seq2seq_format.replace(k, v)
         return [x for x in self.tokenizer.tokenize(seq2seq_format) if x != "▁"]
 
@@ -104,37 +84,32 @@ def format_to_sent(self, text: str) -> Sentence:
                 continue
             try:
                 surf: str = line.split(READING_TOKEN)[0]
-                surf = self.token_to_word[surf] if surf in self.token_to_word else surf
-                for k, v in SPECIAL_TO_RARE.items():
+                surf = self.token2word[surf] if surf in self.token2word else surf
+                for k, v in SPECIAL2RARE.items():
                     surf = surf.replace(k, v)
 
                 reading: str = line.split(READING_TOKEN)[1].split(LEMMA_TOKEN)[0]
-                reading = self.token_to_word[reading] if reading in self.token_to_word else reading
-                for k, v in SPECIAL_TO_RARE.items():
+                reading = self.token2word[reading] if reading in self.token2word else reading
+                for k, v in SPECIAL2RARE.items():
                     reading = reading.replace(k, v)
 
                 lemma: str = line.split(LEMMA_TOKEN)[1].split(CANON_TOKEN)[0]
-                lemma = self.token_to_word[lemma] if lemma in self.token_to_word else lemma
-                for k, v in SPECIAL_TO_RARE.items():
+                lemma = self.token2word[lemma] if lemma in self.token2word else lemma
+                for k, v in SPECIAL2RARE.items():
                     lemma = lemma.replace(k, v)
 
                 canon: str = line.split(CANON_TOKEN)[1]
-                for k, v in self.token_to_word.items():
+                for k, v in self.token2word.items():
                     canon = canon.replace(k, v)
-                for k, v in SPECIAL_TO_RARE.items():
+                for k, v in SPECIAL2RARE.items():
                     canon = canon.replace(k, v)
-                canon = (
-                    f"{self.token_to_word[TRIPLE_DOT_TOKEN]}/{self.token_to_word[TRIPLE_DOT_TOKEN]}"
-                    if canon == f"{TRIPLE_DOT_TOKEN}/{TRIPLE_DOT_TOKEN}"
-                    else canon
-                )
                 canon = f'"代表表記:{canon}"' if canon != NO_CANON_TOKEN else "NIL"
 
                 # 例外処理
-                if surf == " " and reading == "\u3000" and lemma == "\u3000":
-                    surf = "\u3000"
-                if surf == "°C":
-                    surf, lemma, canon = "℃", "℃", '"代表表記:℃/ど"'
+                if surf == " ":
+                    reading = " "
+                    lemma = " "
+                    canon = r'"代表表記:\␣/\␣"'
 
                 formatted += f"{surf} {reading} {lemma} 未定義語 15 その他 1 * 0 * 0 {canon}\n"
             except IndexError:
diff --git a/src/kwja/utils/word_normalization.py b/src/kwja/utils/word_normalization.py
index 757db3ff..4bcdb082 100644
--- a/src/kwja/utils/word_normalization.py
+++ b/src/kwja/utils/word_normalization.py
@@ -26,154 +26,134 @@ def __init__(self) -> None:
     def get_word_norm_op_tags(self, morpheme: Morpheme) -> List[str]:
         try:
             if morpheme.conjtype == "*":
-                normalized = morpheme.lemma
+                norm = morpheme.lemma
             else:
-                normalized = self.jinf(morpheme.lemma, morpheme.conjtype, "基本形", morpheme.conjform)
-            return get_word_norm_op_tags(morpheme.surf, normalized)
+                norm = self.jinf(morpheme.lemma, morpheme.conjtype, "基本形", morpheme.conjform)
+            return get_word_norm_op_tags(morpheme.surf, norm)
         except ValueError as e:
             logger.info(f"failed to get normalized form of {morpheme.surf}: {e}")
             return [IGNORE_WORD_NORM_OP_TAG] * len(morpheme.surf)
 
 
-def get_word_norm_op_tags(surf: str, normalized: str) -> List[str]:
-    surf_len = len(surf) + 1
-    normalized_len = len(normalized) + 1
-    if surf_len < normalized_len:
-        raise ValueError(f"failed to construct normalization labels to convert {surf} to {normalized}")
-    d = np.inf * np.ones((surf_len, normalized_len), dtype=np.int32)
-    dops: List[List[str]] = []
-    for _ in range(surf_len):
-        dops.append([])
-        for _ in range(normalized_len):
-            dops[-1].append("_")
-    d[0, 0] = 0
-    dops[0][0] = ""
-    for j in range(1, normalized_len):
-        cj = normalized[j - 1]
-        for i in range(1, surf_len):
+def get_word_norm_op_tags(surf: str, norm: str) -> List[str]:
+    m, n = len(surf) + 1, len(norm) + 1
+    if m < n:
+        raise ValueError(f"failed to get word norm op tags from {surf} and {norm}")
+    dp: List[List[Tuple[float, str]]] = [[(float("inf"), "_") for _ in range(n)] for _ in range(m)]  # (m, n)
+    dp[0][0] = (0.0, "")
+    for j in range(1, n):
+        cj = norm[j - 1]
+        for i in range(1, m):
             if i < j:
                 continue
             ci = surf[i - 1]
-            lops = []
-            costs = []
-            # heuristics: add D before K
+            candidates = []
+            # heuristic: add D before K
             if ci in CHOON_SET or ci in HATSUON_SET or ci in LOWER2UPPER:
-                lops.append("D")
-                costs.append(d[i - 1, j] + 1)
+                candidates.append((dp[i - 1][j][0] + 1.0, "D"))
             if ci == cj:
-                lops.append("K")
-                costs.append(d[i - 1, j - 1])
+                candidates.append((dp[i - 1][j - 1][0], "K"))
             else:
                 if i == 1 and j == 1 and ci in VOICED2VOICELESS and cj == VOICED2VOICELESS[ci]:
-                    lops.append("V")
-                    costs.append(d[i - 1, j - 1] + 1)
+                    candidates.append((dp[i - 1][j - 1][0] + 1.0, "V"))
                 if ci in LOWER2UPPER and cj == LOWER2UPPER[ci]:
-                    lops.append("S")
-                    costs.append(d[i - 1, j - 1] + 1)
-                if ci in CHOON_SET and i > 1 and dops[i - 1][j - 1] == "K":
+                    candidates.append((dp[i - 1][j - 1][0] + 1.0, "S"))
+                if ci in CHOON_SET and i >= 2 and dp[i - 1][j - 1][1] == "K":
                     # NOTE: "P" and "E" must follow "K"
-                    # jumanpp does not support いくぇー -> いくえい
+                    # Juman++ does not support いくぇー -> いくえい
                     p = surf[i - 2]
                     if p in PROLONGED_MAP and cj == PROLONGED_MAP[p]:
-                        lops.append("P")
-                        costs.append(d[i - 1, j - 1] + 1)
+                        candidates.append((dp[i - 1][j - 1][0] + 1.0, "P"))
                     if p in PROLONGED_MAP_FOR_EROW and cj == PROLONGED_MAP_FOR_EROW[p]:
-                        lops.append("E")
-                        costs.append(d[i - 1, j - 1] + 1)
-            if len(lops) > 0:
-                idx = np.array(costs).argmin()
-                d[i, j] = costs[idx]
-                dops[i][j] = lops[idx]
-    if np.isinf(d[-1, -1]):
-        raise ValueError(f"failed to construct normalization labels to convert {surf} to {normalized}")
+                        candidates.append((dp[i - 1][j - 1][0] + 1.0, "E"))
+            if len(candidates) >= 1:
+                dp[i][j] = sorted(candidates)[0]  # choose the least-cost word norm op tag
+    if dp[-1][-1] == (float("inf"), "_"):
+        raise ValueError(f"failed to get word norm op tags from {surf} and {norm}")
     # backtracking
-    i, j = surf_len - 1, normalized_len - 1
-    ops = []
+    i, j = m - 1, n - 1
+    word_norm_op_tags = []
     while i >= 0 and j >= 0 and not (i == j == 0):
-        op = dops[i][j]
-        assert op not in ("", "_")
-        ops.append(op)
-        if op == "D":
+        word_norm_op_tag = dp[i][j][1]
+        word_norm_op_tags.append(word_norm_op_tag)
+        if word_norm_op_tag == "D":
             i -= 1
         else:
             i -= 1
             j -= 1
-    assert len(ops) == surf_len - 1
-    ops.reverse()
-    return ops
+    word_norm_op_tags.reverse()
+    return word_norm_op_tags
 
 
-def get_normalized(surf: str, ops: List[str], strict: bool = True) -> str:
-    assert len(surf) == len(ops)
-    normalized = ""
-    for i, (c, op) in enumerate(zip(surf, ops)):
-        if op == "K":
-            normalized += c
-        elif op == "V":
-            if strict and i != 0:
+def get_normalized_surf(surf: str, word_norm_op_tags: List[str], strict: bool = True) -> str:
+    assert len(surf) == len(word_norm_op_tags)
+    norm = ""
+    for i, (c, word_norm_op_tag) in enumerate(zip(surf, word_norm_op_tags)):
+        if word_norm_op_tag == "K":
+            norm += c
+        elif word_norm_op_tag == "V":
+            if strict is True and i != 0:
                 raise ValueError(f"not an initial kana {c} in {surf}")
             if c in VOICED2VOICELESS:
-                normalized += VOICED2VOICELESS[c]
+                norm += VOICED2VOICELESS[c]
             else:
-                if strict:
+                if strict is True:
                     raise ValueError(f"not a voiced kana {c} in {surf}")
-                normalized += c
-        elif op == "D":
-            if strict and c not in CHOON_SET and c not in HATSUON_SET and c not in LOWER2UPPER:
+                norm += c
+        elif word_norm_op_tag == "D":
+            if strict is True and c not in CHOON_SET and c not in HATSUON_SET and c not in LOWER2UPPER:
                 raise ValueError(f"not a removable kana {c} in {surf}")
-        elif op == "S":
+        elif word_norm_op_tag == "S":
             if c in LOWER2UPPER:
-                normalized += LOWER2UPPER[c]
+                norm += LOWER2UPPER[c]
             else:
-                if strict:
+                if strict is True:
                     raise ValueError(f"not a small kana {c} in {surf}")
-                normalized += c
-        elif op == "P":
-            # NOTE: in canonical ops, P and E must follow K,
-            # but we do not check this constraint here
-            if len(normalized) <= 0:
-                if strict:
+                norm += c
+        elif word_norm_op_tag == "P":
+            # NOTE: in canonical ops, P and E must follow K, but we do not check this constraint here
+            if len(norm) <= 0:
+                if strict is True:
                     raise ValueError(f"no preceding kana for {c} in {surf}")
-                normalized += c
+                norm += c
             elif c not in CHOON_SET:
-                if strict:
+                if strict is True:
                     raise ValueError(f"not a prolonged sign for {c} in {surf}")
-                normalized += c
+                norm += c
             else:
-                p = normalized[-1]
+                p = norm[-1]
                 if p in PROLONGED_MAP:
-                    normalized += PROLONGED_MAP[p]
+                    norm += PROLONGED_MAP[p]
                 else:
-                    if strict:
+                    if strict is True:
                         raise ValueError(f"not a valid preceding kana {p} in {surf}")
-                    normalized += c
-        elif op == "E":
-            if len(normalized) <= 0:
-                if strict:
+                    norm += c
+        elif word_norm_op_tag == "E":
+            if len(norm) <= 0:
+                if strict is True:
                     raise ValueError(f"no preceding kana for {c} in {surf}")
-                normalized += c
+                norm += c
             elif c not in CHOON_SET:
-                if strict:
+                if strict is True:
                     raise ValueError(f"not a prolonged sign for {c} in {surf}")
-                normalized += c
+                norm += c
             else:
-                p = normalized[-1]
+                p = norm[-1]
                 if p in PROLONGED_MAP_FOR_EROW:
-                    normalized += PROLONGED_MAP_FOR_EROW[p]
+                    norm += PROLONGED_MAP_FOR_EROW[p]
                 else:
-                    if strict:
+                    if strict is True:
                         raise ValueError(f"not a valid preceding kana {p} in {surf}")
-                    normalized += c
+                    norm += c
         else:
-            raise NotImplementedError(f"unknown op {op}")
-    return normalized
+            raise NotImplementedError(f"unknown word norm op tag {word_norm_op_tag}")
+    return norm
 
 
 class SentenceDenormalizer:
-    def __init__(self) -> None:
+    def __init__(self):
         self.mn = MorphemeNormalizer()
         self.md = MorphemeDenormalizer()
-        self._rng = np.random.default_rng()
 
     def denormalize(self, sentence: Sentence, p=0.5) -> None:
         prob = p
@@ -182,7 +162,7 @@ def denormalize(self, sentence: Sentence, p=0.5) -> None:
                 continue
             surf2 = self.md.denormalize(morpheme)
             if surf2 != morpheme.text:
-                if self._rng.random() < prob:
+                if np.random.rand() < prob:
                     morpheme.text = surf2
                     prob *= 0.1
             else:
@@ -196,9 +176,6 @@ def _is_normal_morpheme(self, morpheme):
 
 
 class MorphemeDenormalizer:
-    def __init__(self) -> None:
-        self._rng = np.random.default_rng()
-
     def denormalize(self, morpheme: Morpheme) -> str:
         return self._denormalize(morpheme.surf)
 
@@ -308,10 +285,11 @@ def _denormalize(self, surf: str) -> str:
             return surf2
         probs = np.array(list(map(lambda x: x[1], cands)))
         probs /= probs.sum()
-        idx = self._rng.choice(len(probs), p=probs)
+        idx = np.random.choice(len(probs), p=probs)
         return cands[idx][0]
 
-    def _denormalize_deterministic(self, surf, var_prolonged=False) -> str:
+    @staticmethod
+    def _denormalize_deterministic(surf, var_prolonged=False) -> str:
         # S
         if surf[-1] in UPPER2LOWER:
             surf2 = surf[:-1] + UPPER2LOWER[surf[-1]]
@@ -323,7 +301,7 @@ def _denormalize_deterministic(self, surf, var_prolonged=False) -> str:
             surf2 = surf[:pos] + c + surf[pos + 1 :]
             return surf2
         # P, E
-        if var_prolonged and self._rng.random() < 0.1:
+        if var_prolonged and np.random.rand() < 0.1:
             prolonged = "〜"
         else:
             prolonged = "ー"
diff --git a/tests/cli/test_cli.py b/tests/cli/test_cli.py
index e3677a78..ace6451c 100644
--- a/tests/cli/test_cli.py
+++ b/tests/cli/test_cli.py
@@ -97,7 +97,7 @@ def test_text_input():
         # ("EOD", "EOD\nEOD\n"),  # TODO
         ("おはよう", "おはよう\nEOD\n"),
         ("おはよう．", "おはよう.\nEOD\n"),
-        ("おはよう #今日も一日", "おはよう␣＃今日も一日\nEOD\n"),
+        ("おはよう #今日も一日", "おはよう ＃今日も一日\nEOD\n"),
         ("おはよう。\nこんにちは。\nこんばんわ。\n", "おはよう。こんにちは。こんばんわ。\nEOD\n"),
         ("おはよう。EOD", "おはよう。EOD\nEOD\n"),
     ],
@@ -108,6 +108,19 @@ def test_normalization_and_typo_module(text: str, output: str):
     assert ret.stdout == output
 
 
+@pytest.mark.parametrize(
+    ("text", "output"),
+    [
+        ("今日は${day}日です", "今日は${day}日です"),
+    ],
+)
+def test_normalization_and_char_module(text: str, output: str):
+    ret = runner.invoke(app, args=["--model-size", "tiny", "--tasks", "char", "--text", text])
+    assert ret.exception is None
+    restored_output = "".join([line for line in ret.stdout.splitlines() if not line.startswith("#")]).replace(" ", "")
+    assert restored_output == output.replace(" ", "")
+
+
 def test_file_input():
     with tempfile.NamedTemporaryFile("wt") as f:
         f.write(
@@ -159,7 +172,10 @@ def test_sanity():
         for knp_text in chunk_by_document(io.StringIO(ret.stdout)):
             documents.append(Document.from_knp(knp_text))
         assert len(documents) == 2
-        assert documents[0].text == "KWJAは日本語の統合解析ツールです。汎用言語モデルを利用し、様々な言語解析を統一的な方法で解いています。"
+        assert (
+            documents[0].text
+            == "KWJAは日本語の統合解析ツールです。汎用言語モデルを利用し、様々な言語解析を統一的な方法で解いています。"
+        )
         assert documents[1].text == (
             "計算機による言語理解を実現するためには、計算機に常識・世界知識を与える必要があります。10年前にはこれは非常に難しい問題でしたが、"
             + "近年の計算機パワー、計算機ネットワークの飛躍的進展によって計算機が超大規模テキストを取り扱えるようになり、そこから常識を"
diff --git a/tests/data/datasets/typo_files/0.jsonl b/tests/data/datasets/typo_files/0.jsonl
index 569ca2f5..28a6a72c 100644
--- a/tests/data/datasets/typo_files/0.jsonl
+++ b/tests/data/datasets/typo_files/0.jsonl
@@ -1,2 +1,2 @@
-{"pre_text": "待つの木が枯れる", "post_text": "松の木が枯れる", "kdrs": ["R:松", "D", "K", "K", "K", "K", "K", "K", "K"], "inss": ["_", "_", "_", "_", "_", "_", "_", "_", "_"]}
-{"pre_text": "紹介ことなかった", "post_text": "紹介することがなかった", "kdrs": ["K", "K", "K", "K", "K", "K", "K", "K", "K"], "inss": ["_", "_", "I:する", "_", "I:が", "_", "_", "_", "_"]}
+{"pre_text": "待つの木が枯れる", "post_text": "松の木が枯れる", "kdr_tags": ["R:松", "D", "K", "K", "K", "K", "K", "K", "K"], "ins_tags": ["_", "_", "_", "_", "_", "_", "_", "_", "_"]}
+{"pre_text": "紹介ことなかった", "post_text": "紹介することがなかった", "kdr_tags": ["K", "K", "K", "K", "K", "K", "K", "K", "K"], "ins_tags": ["_", "_", "I:する", "_", "I:が", "_", "_", "_", "_"]}
diff --git a/tests/data/modules/permitted_tokens.json b/tests/data/modules/permitted_tokens.json
index 69814122..838e4c48 100644
--- a/tests/data/modules/permitted_tokens.json
+++ b/tests/data/modules/permitted_tokens.json
@@ -9,7 +9,7 @@
       "input_tokens": ["<pad>"],
       "permitted_tokens": ["<extra_id_0>"]
     },
-    "target_morpheme": "init"
+    "target_property": "init"
   },
   "case002": {
     "surfs": ["計算", "機", "に", "よる", "言語", "理解", "を", "実現", "する"],
@@ -21,7 +21,7 @@
       "input_tokens": ["<pad>", "<extra_id_0>"],
       "permitted_tokens": ["計", "計算"]
     },
-    "target_morpheme": "surf"
+    "target_property": "surf"
   },
   "case003": {
     "surfs": ["計算", "機", "に", "よる", "言語", "理解", "を", "実現", "する"],
@@ -33,7 +33,7 @@
       "input_tokens": ["<pad>", "<extra_id_0>", "計"],
       "permitted_tokens": ["算"]
     },
-    "target_morpheme": "surf"
+    "target_property": "surf"
   },
   "case004": {
     "surfs": ["計算", "機", "に", "よる", "言語", "理解", "を", "実現", "する"],
@@ -45,105 +45,101 @@
       "input_tokens": ["<pad>", "<extra_id_0>", "計算"],
       "permitted_tokens": ["<extra_id_1>"]
     },
-    "target_morpheme": "surf"
+    "target_property": "surf"
   },
   "case005": {
     "surfs": ["計算", "機", "に", "よる", "言語", "理解", "を", "実現", "する"],
     "t5": {
       "input_tokens": ["<pad>", "<extra_id_0>", "計算", "<extra_id_1>"],
       "permitted_tokens": "reading_candidates",
-      "banned_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "</s>"]
+      "prohibited_tokens": ["<extra_id_2>"]
     },
     "mt5": {
       "input_tokens": ["<pad>", "<extra_id_0>", "計算", "<extra_id_1>"],
       "permitted_tokens": "reading_candidates",
-      "banned_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "</s>"]
+      "prohibited_tokens": ["<extra_id_2>"]
     },
-    "target_morpheme": "reading"
+    "target_property": "reading"
   },
   "case006": {
     "surfs": ["計算", "機", "に", "よる", "言語", "理解", "を", "実現", "する"],
     "t5": {
       "input_tokens": ["<pad>", "<extra_id_0>", "計算", "<extra_id_1>", "▁けい"],
-      "permitted_tokens": "reading_candidates",
-      "banned_tokens": ["</s>"]
+      "permitted_tokens": "reading_candidates"
     },
     "mt5": {
       "input_tokens": ["<pad>", "<extra_id_0>", "計算", "<extra_id_1>", "けい"],
-      "permitted_tokens": "reading_candidates",
-      "banned_tokens": ["</s>"]
+      "permitted_tokens": "reading_candidates"
     },
-    "target_morpheme": "reading"
+    "target_property": "reading"
   },
   "case007": {
     "surfs": ["計算", "機", "に", "よる", "言語", "理解", "を", "実現", "する"],
     "t5": {
       "input_tokens": ["<pad>", "<extra_id_0>", "計算", "<extra_id_1>", "▁けい", "さん", "<extra_id_2>"],
       "permitted_tokens": [],
-      "banned_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "</s>"]
+      "prohibited_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "<extra_id_4>", "<extra_id_5>", "<pad>", "</s>"]
     },
     "mt5": {
       "input_tokens": ["<pad>", "<extra_id_0>", "計算", "<extra_id_1>", "けい", "さん", "<extra_id_2>"],
       "permitted_tokens": [],
-      "banned_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "</s>"]
+      "prohibited_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "<extra_id_4>", "<extra_id_5>", "<pad>", "</s>"]
     },
-    "target_morpheme": "lemma"
+    "target_property": "lemma"
   },
   "case008": {
     "surfs": ["計算", "機", "に", "よる", "言語", "理解", "を", "実現", "する"],
     "t5": {
       "input_tokens": ["<pad>", "<extra_id_0>", "計算", "<extra_id_1>", "▁けい", "さん", "<extra_id_2>", "計算"],
       "permitted_tokens": [],
-      "banned_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "</s>"]
+      "prohibited_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_4>", "<extra_id_5>", "<pad>", "</s>"]
     },
     "mt5": {
       "input_tokens": ["<pad>", "<extra_id_0>", "計算", "<extra_id_1>", "けい", "さん", "<extra_id_2>", "計算"],
       "permitted_tokens": [],
-      "banned_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "</s>"]
+      "prohibited_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_4>", "<extra_id_5>", "<pad>", "</s>"]
     },
-    "target_morpheme": "lemma"
+    "target_property": "lemma"
   },
   "case009": {
     "surfs": ["計算", "機", "に", "よる", "言語", "理解", "を", "実現", "する"],
     "t5": {
       "input_tokens": ["<pad>", "<extra_id_0>", "計算", "<extra_id_1>", "▁けい", "さん", "<extra_id_2>", "計算", "<extra_id_3>"],
       "permitted_tokens": [],
-      "banned_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "</s>"]
+      "prohibited_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "<extra_id_5>", "<pad>", "</s>"]
     },
     "mt5": {
       "input_tokens": ["<pad>", "<extra_id_0>", "計算", "<extra_id_1>", "けい", "さん", "<extra_id_2>", "計算", "<extra_id_3>"],
       "permitted_tokens": [],
-      "banned_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "</s>"]
+      "prohibited_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "<extra_id_5>", "<pad>", "</s>"]
     },
-    "target_morpheme": "canon"
+    "target_property": "canon"
   },
   "case010": {
     "surfs": ["計算", "機", "に", "よる", "言語", "理解", "を", "実現", "する"],
     "t5": {
       "input_tokens": ["<pad>", "<extra_id_0>", "計算", "<extra_id_1>", "▁けい", "さん", "<extra_id_2>", "計算", "<extra_id_3>", "計算"],
       "permitted_tokens": [],
-      "banned_tokens": ["<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "</s>"]
+      "prohibited_tokens": ["<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "<extra_id_4>", "<extra_id_5>", "<pad>", "</s>"]
     },
     "mt5": {
       "input_tokens": ["<pad>", "<extra_id_0>", "計算", "<extra_id_1>", "けい", "さん", "<extra_id_2>", "計算", "<extra_id_3>", "計算"],
       "permitted_tokens": [],
-      "banned_tokens": ["<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "</s>"]
+      "prohibited_tokens": ["<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "<extra_id_4>", "<extra_id_5>", "<pad>", "</s>"]
     },
-    "target_morpheme": "canon"
+    "target_property": "canon"
   },
   "case011": {
     "surfs": ["計算", "機", "に", "よる", "言語", "理解", "を", "実現", "する"],
     "t5": {
       "input_tokens": ["<pad>", "<extra_id_0>", "計算", "<extra_id_1>", "▁けい", "さん", "<extra_id_2>", "計算", "<extra_id_3>", "計算", "/", "けい", "さん", "<extra_id_0>"],
-      "permitted_tokens": ["機"],
-      "banned_tokens": ["<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "</s>"]
+      "permitted_tokens": ["機"]
     },
     "mt5": {
       "input_tokens": ["<pad>", "<extra_id_0>", "計算", "<extra_id_1>", "けい", "さん", "<extra_id_2>", "計算", "<extra_id_3>", "計算", "/", "けい", "さん", "<extra_id_0>"],
-      "permitted_tokens": ["機"],
-      "banned_tokens": ["<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "</s>"]
+      "permitted_tokens": ["機"]
     },
-    "target_morpheme": "surf"
+    "target_property": "surf"
   },
   "case012": {
     "surfs": ["計算"],
@@ -155,34 +151,34 @@
       "input_tokens": ["<pad>", "<extra_id_0>", "計算"],
       "permitted_tokens": ["<extra_id_1>"]
     },
-    "target_morpheme": "surf"
+    "target_property": "surf"
   },
   "case013": {
     "surfs": ["計算"],
     "t5": {
       "input_tokens": ["<pad>", "<extra_id_0>", "計算", "<extra_id_1>", "けい", "さん", "<extra_id_2>", "計算", "<extra_id_3>"],
       "permitted_tokens": [],
-      "banned_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "</s>"]
+      "prohibited_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "<extra_id_5>", "<pad>", "</s>"]
     },
     "mt5": {
       "input_tokens": ["<pad>", "<extra_id_0>", "計算", "<extra_id_1>", "けい", "さん", "<extra_id_2>", "計算", "<extra_id_3>"],
       "permitted_tokens": [],
-      "banned_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "</s>"]
+      "prohibited_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "<extra_id_5>", "<pad>", "</s>"]
     },
-    "target_morpheme": "canon"
+    "target_property": "canon"
   },
   "case014": {
     "surfs": ["計算"],
     "t5": {
       "input_tokens": ["<pad>", "<extra_id_0>", "計算", "<extra_id_1>", "けい", "さん", "<extra_id_2>", "計算", "<extra_id_3>", "計算", "/", "けい", "さん"],
       "permitted_tokens": [],
-      "banned_tokens": []
+      "prohibited_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "<extra_id_4>", "<extra_id_5>", "<pad>"]
     },
     "mt5": {
       "input_tokens": ["<pad>", "<extra_id_0>", "計算", "<extra_id_1>", "けい", "さん", "<extra_id_2>", "計算", "<extra_id_3>", "計算", "/", "けい", "さん"],
       "permitted_tokens": [],
-      "banned_tokens": []
+      "prohibited_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "<extra_id_4>", "<extra_id_5>", "<pad>"]
     },
-    "target_morpheme": "canon"
+    "target_property": "canon"
   }
 }
diff --git a/tests/datamodule/datasets/test_word_dataset.py b/tests/datamodule/datasets/test_word_dataset.py
index fac5b160..defe2f9f 100644
--- a/tests/datamodule/datasets/test_word_dataset.py
+++ b/tests/datamodule/datasets/test_word_dataset.py
@@ -78,7 +78,6 @@ def test_encode(data_dir: Path, word_tokenizer: PreTrainedTokenizerBase, dataset
     max_seq_length = 64
     document_split_stride = 1
     dataset = WordDataset(str(path), word_tokenizer, max_seq_length, document_split_stride, **dataset_kwargs)
-    assert dataset.tokenizer_input_format == "text"
     dataset.examples[1].load_discourse_document(Document.from_knp(path.joinpath("1.knp").read_text()))
     num_examples = len(dataset)
 
@@ -368,7 +367,6 @@ def test_split_into_words_encode(
     dataset = WordDataset(
         str(path), split_into_words_word_tokenizer, max_seq_length, document_split_stride, **dataset_kwargs
     )
-    assert dataset.tokenizer_input_format == "words"
     dataset.examples[1].load_discourse_document(Document.from_knp(path.joinpath("1.knp").read_text()))
     num_examples = len(dataset)
 
diff --git a/tests/modules/components/test_logits_processor.py b/tests/modules/components/test_logits_processor.py
index 2218915e..dc610a02 100644
--- a/tests/modules/components/test_logits_processor.py
+++ b/tests/modules/components/test_logits_processor.py
@@ -5,17 +5,15 @@
 
 import pytest
 import torch
-from rhoknp import Sentence
 from transformers import AutoTokenizer, PreTrainedTokenizerFast
 
-from kwja.datamodule.datasets.seq2seq import Seq2SeqFormatter
 from kwja.modules.components.logits_processor import (
-    ForcedLogitsProcessor,
-    TargetMorpheme,
-    get_char2tokens,
-    get_reading_candidates,
+    SurfForcedDecodingLogitsProcessor,
+    TargetProperty,
+    get_char2token_items,
+    get_reading_candidate_token_ids,
 )
-from kwja.utils.constants import FULL_SPACE_TOKEN, HALF_SPACE_TOKEN1, HALF_SPACE_TOKEN2, LEMMA_TOKEN, TRIPLE_DOT_TOKEN
+from kwja.utils.constants import HALF_SPACE_TOKEN, LEMMA_TOKEN
 
 SPECIAL_TOKENS: List[str] = [f"<extra_id_{idx}>" for idx in range(100)]
 
@@ -25,9 +23,9 @@ def test_get_char2tokens() -> None:
         pretrained_model_name_or_path="google/mt5-small",
         additional_special_tokens=SPECIAL_TOKENS,
     )
-    mt5_char2tokens = get_char2tokens(mt5_tokenizer)
-    assert len(mt5_char2tokens) == 19455
-    assert mt5_char2tokens["京"] == {
+    mt5_char2token_items = get_char2token_items(mt5_tokenizer)
+    assert len(mt5_char2token_items) == 19455
+    assert mt5_char2token_items["京"] == {
         "京东": 165392,
         "京娱乐": 178804,
         "京都府": 166766,
@@ -37,8 +35,8 @@ def test_get_char2tokens() -> None:
         "京区": 208641,
         "▁京公网安备": 234066,
     }
-    mt5_underscore_tokens: Set[str] = {x for x in mt5_tokenizer.get_vocab() if x.startswith("▁")}
-    mt5_non_underscore_tokens: Set[str] = {x for x in mt5_tokenizer.get_vocab() if not x.startswith("▁")}
+    mt5_underscore_tokens: Set[str] = {x for x in mt5_tokenizer.vocab if x.startswith("▁")}
+    mt5_non_underscore_tokens: Set[str] = {x for x in mt5_tokenizer.vocab if not x.startswith("▁")}
     assert len(mt5_underscore_tokens) == 56369
     assert len(mt5_non_underscore_tokens) == 193831
 
@@ -46,9 +44,9 @@ def test_get_char2tokens() -> None:
         pretrained_model_name_or_path="retrieva-jp/t5-small-short",
         additional_special_tokens=SPECIAL_TOKENS,
     )
-    t5_char2tokens = get_char2tokens(t5_tokenizer)
-    assert len(t5_char2tokens) == 4289
-    assert t5_char2tokens["京"] == {
+    t5_char2token_items = get_char2token_items(t5_tokenizer)
+    assert len(t5_char2token_items) == 4289
+    assert t5_char2token_items["京"] == {
         "京都府": 3411,
         "京都府出身": 26029,
         "京橋": 22889,
@@ -69,13 +67,13 @@ def test_get_char2tokens() -> None:
         "京阪": 14311,
         "京都市立": 24756,
     }
-    t5_underscore_tokens: Set[str] = {x for x in t5_tokenizer.get_vocab() if x.startswith("▁")}
-    t5_non_underscore_tokens: Set[str] = {x for x in t5_tokenizer.get_vocab() if not x.startswith("▁")}
+    t5_underscore_tokens: Set[str] = {x for x in t5_tokenizer.vocab if x.startswith("▁")}
+    t5_non_underscore_tokens: Set[str] = {x for x in t5_tokenizer.vocab if not x.startswith("▁")}
     assert len(t5_underscore_tokens) == 531
     assert len(t5_non_underscore_tokens) == 31569
 
 
-def test_get_target_morpheme(data_dir: Path) -> None:
+def test_get_target_property(data_dir: Path) -> None:
     model2pretrained_model_name_or_path: Dict[str, str] = {
         "mt5": "google/mt5-small",
         "t5": "retrieva-jp/t5-small-short",
@@ -85,29 +83,29 @@ def test_get_target_morpheme(data_dir: Path) -> None:
             pretrained_model_name_or_path,
             additional_special_tokens=SPECIAL_TOKENS,
         )
-        reading_candidates: Set[int] = get_reading_candidates(tokenizer)
-        char2tokens: Dict[str, Dict[str, int]] = get_char2tokens(tokenizer)
+        reading_candidate_token_ids: List[int] = get_reading_candidate_token_ids(tokenizer)
+        char2token_items: Dict[str, Dict[str, int]] = get_char2token_items(tokenizer)
         test_case_path: Path = data_dir / "modules" / "permitted_tokens.json"
         with open(test_case_path) as f:
             test_cases = json.load(f)
         for test_case in test_cases.values():
-            processor = ForcedLogitsProcessor(
-                surfs=[test_case["surfs"]],
+            processor = SurfForcedDecodingLogitsProcessor(
+                batch_surfs=[test_case["surfs"]],
                 num_beams=1,
                 tokenizer=tokenizer,
-                reading_candidates=reading_candidates,
-                char2tokens=char2tokens,
+                char2token_items=char2token_items,
+                reading_candidate_token_ids=reading_candidate_token_ids,
             )
             input_ids: List[int] = tokenizer.convert_tokens_to_ids(test_case[model]["input_tokens"])
-            target_morpheme: TargetMorpheme = processor._get_target_morpheme(input_ids)
-            assert target_morpheme.surf == (test_case["target_morpheme"] == "surf")
-            assert target_morpheme.reading == (test_case["target_morpheme"] == "reading")
-            assert target_morpheme.lemma == (test_case["target_morpheme"] == "lemma")
-            assert target_morpheme.canon == (test_case["target_morpheme"] == "canon")
+            target_property: TargetProperty = processor._get_target_property(input_ids)
+            assert target_property.surf == (test_case["target_property"] == "surf")
+            assert target_property.reading == (test_case["target_property"] == "reading")
+            assert target_property.lemma == (test_case["target_property"] == "lemma")
+            assert target_property.canon == (test_case["target_property"] == "canon")
 
 
 @pytest.mark.parametrize(
-    ("input_tokens", "surfs", "expected_remaining_surf"),
+    ("input_tokens", "surfs", "expected_ungenerated_surf"),
     [
         (["<pad>", "<extra_id_0>"], ["研究", "する"], "研究"),
         (["<pad>", "<extra_id_0>", "研"], ["研究", "する"], "究"),
@@ -151,83 +149,51 @@ def test_get_target_morpheme(data_dir: Path) -> None:
         ),
     ],
 )
-def test_get_remaining_surf(input_tokens: List[str], surfs: List[str], expected_remaining_surf: str) -> None:
+def test_get_ungenerated_surf(input_tokens: List[str], surfs: List[str], expected_ungenerated_surf: str) -> None:
     for pretrained_model_name_or_path in ["google/mt5-small", "retrieva-jp/t5-small-short"]:
         tokenizer: PreTrainedTokenizerFast = AutoTokenizer.from_pretrained(
             pretrained_model_name_or_path=pretrained_model_name_or_path,
             additional_special_tokens=SPECIAL_TOKENS,
         )
-        char2tokens = get_char2tokens(tokenizer)
-        reading_candidates = get_reading_candidates(tokenizer)
+        char2token_items = get_char2token_items(tokenizer)
+        reading_candidate_token_ids = get_reading_candidate_token_ids(tokenizer)
 
-        processor = ForcedLogitsProcessor(
-            surfs=[surfs],
+        processor = SurfForcedDecodingLogitsProcessor(
+            batch_surfs=[surfs],
             num_beams=1,
             tokenizer=tokenizer,
-            reading_candidates=reading_candidates,
-            char2tokens=char2tokens,
+            char2token_items=char2token_items,
+            reading_candidate_token_ids=reading_candidate_token_ids,
         )
         input_ids: List[int] = tokenizer.convert_tokens_to_ids(input_tokens)
-        assert processor._get_remaining_surf(input_ids, surfs) == expected_remaining_surf
+        assert processor._get_ungenerated_surf(input_ids, surfs) == expected_ungenerated_surf
 
 
 @pytest.mark.parametrize(
-    ("surfs", "permitted_tokens"),
+    ("surfs", "expected_permitted_tokens"),
     [
         (["研究", "を", "する"], ["研究", "研"]),
-        ([FULL_SPACE_TOKEN, "研究", "を", "する"], [FULL_SPACE_TOKEN]),
-        ([HALF_SPACE_TOKEN1, "研究", "を", "する"], [HALF_SPACE_TOKEN1]),
-        ([HALF_SPACE_TOKEN2, "研究", "を", "する"], [HALF_SPACE_TOKEN2]),
-        ([TRIPLE_DOT_TOKEN, "研究", "を", "する"], [TRIPLE_DOT_TOKEN]),
+        ([HALF_SPACE_TOKEN, "研究", "を", "する"], [HALF_SPACE_TOKEN]),
     ],
 )
-def test_get_banned_token_ids(surfs: List[str], permitted_tokens: List[str]) -> None:
+def test_get_permitted_token_ids(surfs: List[str], expected_permitted_tokens: List[str]) -> None:
     for pretrained_model_name_or_path in ["google/mt5-small", "retrieva-jp/t5-small-short"]:
         tokenizer: PreTrainedTokenizerFast = AutoTokenizer.from_pretrained(
             pretrained_model_name_or_path,
             additional_special_tokens=SPECIAL_TOKENS,
         )
-        reading_candidates: Set[int] = get_reading_candidates(tokenizer)
-        char2tokens: Dict[str, Dict[str, int]] = get_char2tokens(tokenizer)
-        expected_banned_token_ids: Set[int] = set(tokenizer.get_vocab().values()) - set(
-            tokenizer.convert_tokens_to_ids(permitted_tokens)
-        )
+        char2token_items = get_char2token_items(tokenizer)
+        reading_candidate_token_ids = get_reading_candidate_token_ids(tokenizer)
 
-        processor = ForcedLogitsProcessor(
-            surfs=[surfs],
+        processor = SurfForcedDecodingLogitsProcessor(
+            batch_surfs=[surfs],
             num_beams=2,
             tokenizer=tokenizer,
-            reading_candidates=reading_candidates,
-            char2tokens=char2tokens,
+            char2token_items=char2token_items,
+            reading_candidate_token_ids=reading_candidate_token_ids,
         )
-        banned_token_ids: Set[int] = processor._get_banned_token_ids("".join(surfs))
-        assert sorted(list(banned_token_ids)) == sorted(list(expected_banned_token_ids))
-
-
-def test_get_generated_surf(data_dir: Path) -> None:
-    for pretrained_model_name_or_path in ["google/mt5-small", "retrieva-jp/t5-small-short"]:
-        tokenizer: PreTrainedTokenizerFast = AutoTokenizer.from_pretrained(
-            pretrained_model_name_or_path=pretrained_model_name_or_path,
-            additional_special_tokens=SPECIAL_TOKENS,
-        )
-        formatter: Seq2SeqFormatter = Seq2SeqFormatter(tokenizer)
-        char2tokens = get_char2tokens(tokenizer)
-        reading_candidates = get_reading_candidates(tokenizer)
-
-        test_case_dir: Path = data_dir / "modules" / "juman"
-        for path in test_case_dir.glob("*.juman"):
-            with path.open() as f:
-                sentence: Sentence = Sentence.from_jumanpp(f.read())
-                processor = ForcedLogitsProcessor(
-                    surfs=[formatter.get_surfs(sentence)],
-                    num_beams=2,
-                    tokenizer=tokenizer,
-                    reading_candidates=reading_candidates,
-                    char2tokens=char2tokens,
-                )
-                tgt_tokens: List[str] = formatter.get_tgt_tokens(sentence)
-                tgt_input_ids: List[int] = [*tokenizer.convert_tokens_to_ids(tgt_tokens), tokenizer.eos_token_id]
-                assert processor.surfs[0] == processor._get_generated_surf(tgt_input_ids)
+        permitted_token_ids: List[int] = processor._get_permitted_token_ids("".join(surfs))
+        assert sorted(permitted_token_ids) == sorted(tokenizer.convert_tokens_to_ids(expected_permitted_tokens))
 
 
 def test_get_mask(data_dir: Path) -> None:
@@ -241,22 +207,25 @@ def test_get_mask(data_dir: Path) -> None:
             additional_special_tokens=SPECIAL_TOKENS,
         )
         vocab_size: int = len(tokenizer.get_vocab())
-        reading_candidates: Set[int] = get_reading_candidates(tokenizer)
-        reading_candidate_tokens: Set[str] = {tokenizer.convert_ids_to_tokens([x])[0] for x in reading_candidates}
-        char2tokens = get_char2tokens(tokenizer)
-        all_tokens: Set[str] = set(tokenizer.get_vocab().keys())
+        char2token_items = get_char2token_items(tokenizer)
+        reading_candidate_token_ids = get_reading_candidate_token_ids(tokenizer)
+        reading_candidate_tokens: Set[str] = {
+            tokenizer.convert_ids_to_tokens(reading_candidate_token_id)
+            for reading_candidate_token_id in reading_candidate_token_ids
+        }
+        all_tokens: Set[str] = set(tokenizer.vocab.keys())
 
         test_case_path: Path = data_dir / "modules" / "permitted_tokens.json"
         with open(test_case_path) as f:
             test_cases = json.load(f)
-        for test_case in test_cases.values():
-            assert test_case["target_morpheme"] in ["surf", "reading", "lemma", "canon", "init"]
-            processor = ForcedLogitsProcessor(
-                surfs=[test_case["surfs"]],
+        for k, test_case in test_cases.items():
+            assert test_case["target_property"] in ["surf", "reading", "lemma", "canon", "init"]
+            processor = SurfForcedDecodingLogitsProcessor(
+                batch_surfs=[test_case["surfs"]],
                 num_beams=1,
                 tokenizer=tokenizer,
-                reading_candidates=reading_candidates,
-                char2tokens=char2tokens,
+                char2token_items=char2token_items,
+                reading_candidate_token_ids=reading_candidate_token_ids,
             )
             warped_scores: Optional[torch.Tensor] = None
             for idx in range(1, len(test_case[model]["input_tokens"]) + 1):
@@ -276,11 +245,11 @@ def test_get_mask(data_dir: Path) -> None:
             if test_case[model]["permitted_tokens"] == "reading_candidates":
                 expected_permitted_tokens: Set[str] = reading_candidate_tokens
                 expected_permitted_tokens.add(LEMMA_TOKEN)
-            elif not test_case[model]["permitted_tokens"]:
+            elif len(test_case[model]["permitted_tokens"]) == 0:
                 expected_permitted_tokens = copy.deepcopy(all_tokens)
             else:
                 expected_permitted_tokens = set(test_case[model]["permitted_tokens"])
 
-            if "banned_tokens" in test_case[model]:
-                expected_permitted_tokens -= set(test_case[model]["banned_tokens"])
+            if "prohibited_tokens" in test_case[model]:
+                expected_permitted_tokens -= set(test_case[model]["prohibited_tokens"])
             assert sorted(list(permitted_tokens)) == sorted(list(expected_permitted_tokens))
diff --git a/tests/utils/test_reading_prediction.py b/tests/utils/test_reading_prediction.py
index c6eadd63..be30214a 100644
--- a/tests/utils/test_reading_prediction.py
+++ b/tests/utils/test_reading_prediction.py
@@ -33,7 +33,7 @@
                 [False, False, False, False, True],
                 [False, False, False, False, False],
             ],
-            ["ふせい", "␣", "な", "にゅうりょく"],
+            ["ふせい", "_", "な", "にゅうりょく"],
         ),
         (
             ["[UNK]", "ふせい", "[ID]", "[ID]", "にゅうりょく"],
diff --git a/tests/utils/test_seq2seq_format.py b/tests/utils/test_seq2seq_format.py
index f90851a0..9a875c5a 100644
--- a/tests/utils/test_seq2seq_format.py
+++ b/tests/utils/test_seq2seq_format.py
@@ -8,15 +8,14 @@
 
 from kwja.utils.constants import (
     CANON_TOKEN,
-    FULL_SPACE_TOKEN,
-    HALF_SPACE_TOKEN1,
+    HALF_SPACE_TOKEN,
     LEMMA_TOKEN,
-    MORPHEME_SPLIT_TOKEN,
+    MORPHEME_DELIMITER_TOKEN,
     NO_CANON_TOKEN,
     READING_TOKEN,
     SURF_TOKEN,
-    TRIPLE_DOT_TOKEN,
 )
+from kwja.utils.normalization import normalize_morpheme
 from kwja.utils.seq2seq_format import Seq2SeqFormatter
 
 seq2seq_formats: List[str] = [
@@ -36,7 +35,7 @@
     dedent(
         """\
         また また また 又/また
-        ， ， ， ，/，
+        , , , ,/,
         校区 こうく 校区 校区/こうく
         で で で で/で
         行わ おこなわ 行う 行う/おこなう
@@ -58,12 +57,12 @@
         """
     ),
     dedent(
-        f"""\
+        """\
         「 「 「 「/「
         核 かく 核 核/かく
         の の の の/の
         歴史 れきし 歴史 歴史/れきし
-        {TRIPLE_DOT_TOKEN} {TRIPLE_DOT_TOKEN} {TRIPLE_DOT_TOKEN} {TRIPLE_DOT_TOKEN}/{TRIPLE_DOT_TOKEN}
+        ... ... ... .../...
         ヒロシマ ひろしま ヒロシマ ヒロシマ/ひろしま
         、 、 、 、/、
         ナガサキ ながさき ナガサキ ナガサキ/ながさき
@@ -77,21 +76,21 @@
         f"""\
         後 あと 後 後/あと
         一 ついたち 一 一/いち
-        日 {FULL_SPACE_TOKEN} 日 日/にち
+        日 {HALF_SPACE_TOKEN} 日 日/にち
         まで まで まで まで/まで
         ！ ！ ！ ！/！
         ？ ？ ？ ？/？
         . . . ./.
         / / / ///
-        ℃ ど ℃ ℃/ど
+        °C ど °C °C/ど
         """
     ),
     dedent(
         f"""\
-        ＪＵＭＰ ＪＵＭＰ ＪＵＭＰ ＪＵＭＰ/ＪＵＭＰ
-        {FULL_SPACE_TOKEN} {FULL_SPACE_TOKEN} {FULL_SPACE_TOKEN} /
-        ＣＯＭＩＣＳ ＣＯＭＩＣＳ ＣＯＭＩＣＳ ＣＯＭＩＣＳ/ＣＯＭＩＣＳ
-        {HALF_SPACE_TOKEN1} {HALF_SPACE_TOKEN1} {HALF_SPACE_TOKEN1} /
+        JUMP JUMP JUMP JUMP/JUMP
+        {HALF_SPACE_TOKEN} {HALF_SPACE_TOKEN} {HALF_SPACE_TOKEN} /
+        COMICS COMICS COMICS COMICS/COMICS
+        {HALF_SPACE_TOKEN} {HALF_SPACE_TOKEN} {HALF_SPACE_TOKEN} /
         """
     ),
 ]
@@ -101,16 +100,18 @@ def test_get_surfs(data_dir: Path, seq2seq_tokenizer: PreTrainedTokenizerFast):
     surfs: List[str] = [
         "計算 機 に よる 言語 理解 を 実現 する",
         "また , 校区 で 行わ れる 事業 や 防犯 など 校区 の 情報 も 記載 さ れて い ます 。",
-        f"「 核 の 歴史 {TRIPLE_DOT_TOKEN} ヒロシマ 、 ナガサキ を 超えて 」 。",
+        "「 核 の 歴史 ... ヒロシマ 、 ナガサキ を 超えて 」 。",
         "後 一 日 まで ! ? . / °C",
-        f"JUMP {FULL_SPACE_TOKEN} COMICS {HALF_SPACE_TOKEN1}",
+        f"JUMP {HALF_SPACE_TOKEN} COMICS {HALF_SPACE_TOKEN}",
     ]
     seq2seq_formatter = Seq2SeqFormatter(seq2seq_tokenizer)
     juman_dir: Path = data_dir / "modules" / "juman"
     for idx, path in enumerate(sorted(juman_dir.glob("*.juman"))):
         with open(path) as f:
-            sent = Sentence.from_jumanpp(f.read())
-            assert seq2seq_formatter.get_surfs(sent) == surfs[idx].split(" ")
+            sentence = Sentence.from_jumanpp(f.read())
+            for morpheme in sentence.morphemes:
+                normalize_morpheme(morpheme)
+            assert seq2seq_formatter.get_surfs(sentence) == surfs[idx].split(" ")
 
 
 def test_get_src_tokens(data_dir: Path, seq2seq_tokenizer: PreTrainedTokenizerFast):
@@ -138,20 +139,22 @@ def test_get_src_tokens(data_dir: Path, seq2seq_tokenizer: PreTrainedTokenizerFa
             "ます",
             "。",
         ],
-        ["「", "核", "の", "歴史", TRIPLE_DOT_TOKEN, "ヒロ シマ", "、", "ナ ガ サ キ", "を", "超 えて", "」", "。"],
+        ["「", "核", "の", "歴史", "...", "ヒロ シマ", "、", "ナ ガ サ キ", "を", "超 えて", "」", "。"],
         ["後", "一", "日", "まで", "!", "?", ".", "/", "° C"],
-        ["J UMP", FULL_SPACE_TOKEN, "COM ICS", HALF_SPACE_TOKEN1],
+        ["J UMP", HALF_SPACE_TOKEN, "COM ICS", HALF_SPACE_TOKEN],
     ]
     seq2seq_formatter = Seq2SeqFormatter(seq2seq_tokenizer)
     juman_dir: Path = data_dir / "modules" / "juman"
     for idx, path in enumerate(sorted(juman_dir.glob("*.juman"))):
         with open(path) as f:
-            sent = Sentence.from_jumanpp(f.read())
+            sentence = Sentence.from_jumanpp(f.read())
+            for morpheme in sentence.morphemes:
+                normalize_morpheme(morpheme)
             expected_src_tokens: List[str] = []
             for morphemes in src_tokens[idx]:
-                expected_src_tokens.extend([*morphemes.split(" "), MORPHEME_SPLIT_TOKEN])
+                expected_src_tokens.extend([*morphemes.split(" "), MORPHEME_DELIMITER_TOKEN])
             assert [
-                x[1:] if x.startswith("▁") else x for x in seq2seq_formatter.get_src_tokens(sent)
+                x[1:] if x.startswith("▁") else x for x in seq2seq_formatter.get_src_tokens(sentence)
             ] == expected_src_tokens[:-1]
 
 
@@ -160,7 +163,9 @@ def test_get_tgt_tokens(data_dir: Path, seq2seq_tokenizer: PreTrainedTokenizerFa
     juman_dir: Path = data_dir / "modules" / "juman"
     for idx, path in enumerate(sorted(juman_dir.glob("*.juman"))):
         with open(path) as f:
-            sent = Sentence.from_jumanpp(f.read())
+            sentence = Sentence.from_jumanpp(f.read())
+            for morpheme in sentence.morphemes:
+                normalize_morpheme(morpheme)
             expected_seq2seq_format: str = ""
             for line in seq2seq_formats[idx].rstrip("\n").split("\n"):
                 mrphs: List[str] = line.split(" ")
@@ -170,7 +175,7 @@ def test_get_tgt_tokens(data_dir: Path, seq2seq_tokenizer: PreTrainedTokenizerFa
             expected_tgt_tokens: List[str] = [
                 x for x in seq2seq_tokenizer.tokenize(expected_seq2seq_format) if x != "▁"
             ]
-            assert seq2seq_formatter.get_tgt_tokens(sent) == expected_tgt_tokens
+            assert seq2seq_formatter.get_tgt_tokens(sentence) == expected_tgt_tokens
 
 
 def test_format_to_sent(data_dir: Path, seq2seq_tokenizer: PreTrainedTokenizerFast):
@@ -178,7 +183,9 @@ def test_format_to_sent(data_dir: Path, seq2seq_tokenizer: PreTrainedTokenizerFa
     juman_dir: Path = data_dir / "modules" / "juman"
     for idx, path in enumerate(sorted(juman_dir.glob("*.juman"))):
         with open(path) as f:
-            expected_sent = Sentence.from_jumanpp(f.read())
+            expected_sentence = Sentence.from_jumanpp(f.read())
+            for morpheme in expected_sentence.morphemes:
+                normalize_morpheme(morpheme)
 
         seq2seq_format: List[str] = []
         for line in seq2seq_formats[idx].rstrip("\n").split("\n"):
@@ -188,15 +195,19 @@ def test_format_to_sent(data_dir: Path, seq2seq_tokenizer: PreTrainedTokenizerFa
                     " "
                 )
             )
-        actual_sent: Sentence = seq2seq_formatter.format_to_sent("".join(seq2seq_format))
-        assert len(actual_sent.morphemes) == len(expected_sent.morphemes)
-        for actual_morpheme, expected_morpheme in zip(actual_sent.morphemes, expected_sent.morphemes):
+
+        actual_sentence: Sentence = seq2seq_formatter.format_to_sent("".join(seq2seq_format))
+        assert len(actual_sentence.morphemes) == len(expected_sentence.morphemes)
+        for actual_morpheme, expected_morpheme in zip(actual_sentence.morphemes, expected_sentence.morphemes):
             if "/" in expected_morpheme.reading and len(expected_morpheme.reading) > 1:
                 expected_reading: str = expected_morpheme.reading.split("/")[0]
             else:
                 expected_reading = expected_morpheme.reading
 
-            actual_canon: str = str(actual_morpheme.canon) if actual_morpheme.canon != NO_CANON_TOKEN else "NIL"
+            if actual_morpheme.canon == r"\␣/\␣":
+                actual_canon: str = "/"
+            else:
+                actual_canon = str(actual_morpheme.canon) if actual_morpheme.canon != NO_CANON_TOKEN else "NIL"
             expected_canon: str = expected_morpheme.canon if expected_morpheme.canon is not None else "NIL"
 
             if expected_morpheme.pos == "特殊":
diff --git a/tests/utils/test_word_normalization.py b/tests/utils/test_word_normalization.py
index 8ce07a46..0a7e5555 100644
--- a/tests/utils/test_word_normalization.py
+++ b/tests/utils/test_word_normalization.py
@@ -4,7 +4,7 @@
 from kwja.utils.word_normalization import (
     MorphemeDenormalizer,
     MorphemeNormalizer,
-    get_normalized,
+    get_normalized_surf,
     get_word_norm_op_tags,
 )
 
@@ -73,7 +73,7 @@
 
 @pytest.mark.parametrize(("surf", "ops", "expected"), wellformed_list)
 def test_gen_normalized_surf(surf, ops, expected):
-    assert get_normalized(surf, ops, strict=True) == expected
+    assert get_normalized_surf(surf, ops, strict=True) == expected
 
 
 @pytest.mark.parametrize(("surf", "expected", "normalized"), wellformed_list)
@@ -93,12 +93,12 @@ def test_get_normalization_opns(surf, expected, normalized):
 @pytest.mark.parametrize(("surf", "ops", "expected"), malformed_list)
 def test_gen_normalized_surf_malformed(surf, ops, expected):
     with pytest.raises(ValueError):
-        get_normalized(surf, ops, strict=True)
+        get_normalized_surf(surf, ops, strict=True)
 
 
 @pytest.mark.parametrize(("surf", "ops", "expected"), malformed_list)
 def test_gen_normalized_surf_malformed_loose(surf, ops, expected):
-    assert get_normalized(surf, ops, strict=False) == expected
+    assert get_normalized_surf(surf, ops, strict=False) == expected
 
 
 def test_morpheme_normalizer():