Neural Graph-based Dependency Parser

In this paper, we implement a neural graph-based dependency parser inspired by those of Kiperwasser and Goldberg (Kiperwasser and Goldberg, 2016) and Dozat and Manning (Dozat and Manning, 2017). We train and test our parser on the English and Hindi Treebanks from the Universal Dependencies Project, achieving a UAS of 84.80% and an LAS of 78.61% on the English corpus, and a UAS of 91.92% and an LAS of 83.94% on the Hindi corpus.

Authors

• Jack Harding
• Bram Kooiman
• Akash Raj

User guide

• To test the datasets, navigate to src/ folder and run python testing.py
• To train the datasets, navigate to src/ folder and run python main.py

[NOTE] - By default, the training and testing is done on english_full dataset. To train/test on other datasets, please change the path in testing.py and main.py to one of (english_full, english_short, hindi_full, hindi_short)

Dataset

CoNLL-U files from universal-depenedencis project (for english and hindi) have been used (converted to json format). All single word occurences are converted to <unk>. english_short contains sentences with a maximum of 12 words. hindi_short contains sentences with a maximum of 17 words. For each sentence, we added an extra word <ROOT> for convenience.

Docs

• data/ folder contains hindi and english datasets from universal-dependencies project. It also contains the latest_weights (BiLSTM network weights trained with the training datasets)
• src/ folder contains the source code for the project.
  • main.py is used train the network
  • testing.py is used to calculate the UAS and LAS scores for test dataset
  • MST_FINAL.py contains the implementation of Chu Liu Edmonds algorithm
  • data_cleanup.py and utils.py contain utility functions for data cleanup and manipulation

Results

Convergence of loss on the english_full dataset:

Heatmaps of the hindi dataset (sentence 4 with 17 words) for gold tree, and after epochs 0 and 29.