SyntacticPointer

This repository includes a more efficient implementation of the left-to-right dependency parser with pointer networks described in NAACL paper Left-to-Right Dependency Parsing with Pointer Networks. This is based on the updated version of the framework by Ma et al. (2018) (https://github.com/XuezheMax/NeuroNLP2) that implements a faster batch decoding and includes a "parse" mode to test a saved model. Unlike the original implementation (https://github.com/danifg/Left2Right-Pointer-Parser), this optimized version does not avoid the generation of cycles during decoding, since the presence of them is negligible. We additionally include an option to remove cycles as a post-processing step in order to output a well-formed dependency tree.

Requirements

This implementation requires Python 3.6, PyTorch >=1.0.0, Gensim >= 0.12.0.

Experiments

First, please go to the experiments folder:

   cd experiments/

Then, to train the parser, just include the paths for data and embeddings in the following script, and run:

./scripts/train.sh <model_name>

and, to test an already-trained model, just run:

./scripts/test.sh <model_name>

Please add the argument --remove_cycles to the test script if you want to output dependency trees without cycles.

Performance

We report a fair comparison against the DeepBiaffine (Deep Biaffine Attention for Neural Dependency Parsing) and StackPointer (Stack-Pointer Networks for Dependency Parsing) parsers (both also implemented under the framework by Ma et al. (2018) (https://github.com/XuezheMax/NeuroNLP2)) on the Stanford Dependency conversion (v3.3.0) of the English Penn Treebank with POS tags predicted by Stanford POS tagger.

Parser	UAS	LAS	Speed (sents/s)
DeepBiaffine	95.91	94.31	103.29
StackPointer	95.90	94.26	84.42
SyntacticPointer	96.02	94.39	194.21

While less accurate than the original parser (https://github.com/danifg/Left2Right-Pointer-Parser) due to the lack of cycle detection during decoding, this alternative implementation outperforms the other two parsers in both accuracy and speed.

Citation

@inproceedings{fernandez-gonzalez-gomez-rodriguez-2019-left,
        title = "Left-to-Right Dependency Parsing with Pointer Networks",
    author = "Fern{\'a}ndez-Gonz{\'a}lez, Daniel  and G{\'o}mez-Rodr{\'\i}guez, Carlos",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational    Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/N19-1076",
    doi = "10.18653/v1/N19-1076",
    pages = "710--716"
    }

Acknowledgments

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), from MINECO (FFI2014-51978-C2-2-R, TIN2017-85160-C2-1-R) and from Xunta de Galicia (ED431B 2017/01).