lstm-parser

Transition based dependency parser with state embeddings computed by LSTM RNNs and training with exploration as presented in this EMNLP 2016 paper: http://www.aclweb.org/anthology/D/D16/D16-1211.pdf

See https://github.com/clab/lstm-parser for more information (https://github.com/clab/lstm-parser is the parser with static oracles presented in ACL 2015)

Required software

• A C++ compiler supporting the C++11 language standard
• Boost libraries
• Eigen (newer versions strongly recommended)
• CMake
• gcc (only tested with gcc version 5.3.0, may be incompatible with earlier versions)

Build instructions

mkdir build
cd build
cmake .. -DEIGEN3_INCLUDE_DIR=/path/to/eigen
make -j2

Train a parsing model

Having a training.conll file and a development.conll formatted according to the CoNLL data format, to train a parsing model with the LSTM parser type the following at the command line prompt:

java -jar ParserOracleArcStd.jar -t -1 -l 1 -c training.conll > trainingOracle.txt
java -jar ParserOracleArcStd.jar -t -1 -l 1 -c development.conll > devOracle.txt

parser/lstm-parse -T trainingOracle.txt -d devOracle.txt --hidden_dim 100 --lstm_input_dim 100 -w sskip.100.vectors --pretrained_dim 100 --rel_dim 20 --action_dim 20 -t -P

Link to the word vectors that we used in the ACL 2015 paper for English: sskip.100.vectors.

Note-1: you can also run it without word embeddings by removing the -w option for both training and parsing.

Note-2: the training process should be stopped when the development result does not substantially improve anymore. Normally, after 5500 iterations.

Note-3: the parser reports (after each iteration) results including punctuation symbols while in the ACL-15 and the EMNLP-16 paper we report results excluding them (as it is common practice in those data sets). You can find eval.pl script from the CoNLL-X Shared Task to get the correct numbers.

Parse data with your parsing model

Having a test.conll file formatted according to the CoNLL data format

java -jar ParserOracleArcStd.jar -t -1 -l 1 -c test.conll > testOracle.txt

parser/lstm-parse -T trainingOracle.txt -d testOracle.txt --hidden_dim 100 --lstm_input_dim 100 -w sskip.100.vectors --pretrained_dim 100 --rel_dim 20 --action_dim 20 -P -m parser_pos_2_32_100_20_100_12_20-pidXXXX.params

The model name/id is stored where the parser has been trained. The parser will output the conll file with the parsing result.

Citation

If you make use of this software, please cite the following:

@InProceedings{ballesteros-EtAl:2016:EMNLP2016,
  author    = {Ballesteros, Miguel  and  Goldberg, Yoav  and  Dyer, Chris  and  Smith, Noah A.},
  title     = {Training with Exploration Improves a Greedy Stack LSTM Parser},
  booktitle = {Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing},
  month     = {November},
  year      = {2016},
  address   = {Austin, Texas},
  publisher = {Association for Computational Linguistics},
  pages     = {2005--2010},
  url       = {https://aclweb.org/anthology/D16-1211}
}

License

This software is released under the terms of the Apache License, Version 2.0.

Contact

For questions and usage issues, please contact miguelballesteros1@gmail.com and yoav.goldberg@gmail.com