This implement is a simplified version which removes some unnecessary flag and applies nn Module in Pytorch to construct LSTM network instead of LSTMCell. Besides, some more tags are supported and you can refer it from option list.
The techniques behind the parser are described in the paper Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations.
- Python 3.x interpreter
- Pytorch library
The software requires having a training.conll and development.conll files formatted according to the CoNLL data format, or a training.conllu and development.conllu files formatted according to the CoNLLU data format.
python src/parser.py --outdir [results directory] --train training.conll --dev development.conll --epochs 30 --lstmdims 125 --lstmlayers 2 [--extrn extrn.vectors]
The command for parsing a test.conll file formatted according to the CoNLL data format with a previously trained model is:
python src/parser.py --predict --outdir [results directory] --test test.conll [--extrn extrn.vectors] --model [trained model file] --params [param file generate during training]
The parser will store the resulting conll file in the out directory (--outdir).
- The multiple roots checking of the evaluation script is turned off (See here) as it might generate trees with multiple roots. (See the discussion here)
- This version delete some unnecessary flag and set the bi-LSTM to be mandatory(2 bi-LSTM layer)
- You can refer forward attribute in mst-parser model for dropout rate of different components.
- Anything you think can improve performance please contact and discuss with me.