A bidirectional LSTM implementation of the Covington (2001) algorithm with dynamic oracle for non-projective transition-based dependency parsing.
BIST-covington is a non-projective parser based on BIST-parsers (Kiperwasser and Goldberg, 2016)
For segmentation and part-of-speech tagging the current system relies on the output provided by UDpipe (Straka et al., 2016)
Python 2.7 interpreter
The basic command to train a BIST-covington is pretty similar to the one use to train a BIST-parser:
python train.py \
--dynet-seed 123456789 \
--dynet-mem $DYNET_MEM \
--epochs 30 \
--model $NAME_OUTPUT_MODEL \
--conf $PATH_TO_configuration.yml \
--outdir $OUTDIR \
--train $PATH_TO_TRAIN_CONLLU_FILE \
--dev $PATH_TO_DEV_CONLLU_FILE \
--k1 $K1 \
--kb $KB \
--lstmdims $LSTMDIMS \
--wembedding $WEMBEDDING \
--userl \
--usehead \
For a description of all available options (external embeddings, scope of the windows, ...) type:
python train.py --help
To run a trained model:
python run_model.py \
-p $PATH_PARAMS \
-m $PATH_MODEL \
-o $PATH_OUTPUT
-epe $PATH_POS_EMBEDDINGS \
-efe $PATH_FEATS_EMMBEDDINGS \
-ewe $PATH_EMBEDDINGS \
-r [raw|conllu] \
-i $PATH_INPUT \
--dynet-mem $DYNET_MEM \
-udpipe_bin $PAHT_UDPIPE_BIN \
-udpipe_model $PATH_UDPIPE_MODEL
To appear
@inproceedings{bist-covington,
author = {David Vilares and Carlos G\'{o}mez-Rodr\'{\i}guez},
title = {{A non-projective greedy dependency parser with bidirectional LSTMs}},
booktitle = {{Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies}},
publisher = {Association for Computational Linguistics},
pages = {1--10},
location = {Vancouver, Canada},
year={2017}
}
This software is released under the terms of the Apache License, Version 2.0.
This work has received funding from the European Research Council (ERC), under the European Union's Horizon 2020 research and innovation programme (FASTPARSE, grant agreement No 714150).
Kiperwasser, E., & Goldberg, Y. (2016). Simple and accurate dependency parsing using bidirectional LSTM feature representations. arXiv preprint arXiv:1603.04351.
Straka, M., Hajic, J., & Straková, J. (2016). UD-Pipe: Trainable pipeline for processing CoNLL-U files performing tokenization, morphological analysis, POS tagging and parsing. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016).
Gómez-Rodríguez, C., & Fernández-González, D. (2015). An efficient dynamic oracle for unrestricted non-projective parsing. Volume 2: Short Papers, 256.
Nivre, J. (2008). Algorithms for deterministic incremental dependency parsing. Computational Linguistics, 34(4), 513-553.
Covington, M. A. (2001). A fundamental algorithm for dependency parsing. In Proceedings of the 39th annual ACM southeast conference (pp. 95-102).