This is the source code of our dependency parser proposed in paper “Hierarchical Pointer Net Parsing” accepted by EMNLP 2019. Git Repository: https://github.com/ntunlp/ptrnet-depparser.git

Requirements

Python 2.7, PyTorch >=0.3.0, Gensim >= 0.12.0

Models

We have implemented the below models in this project, which can be found in ./neuronlp2/models/parsing2.py:

• HPtrNetPSTGate: In each step, decoder receives hidden states from sibling, parent and previous step. Use Gate described in the paper.

• HPtrNetPSTSGate: In each step, decoder receives hidden states from sibling, parent and previous step. Use SGate described in the paper.

• HPtrNetPSGate: In each step, decoder receives hidden states from sibling and parent. Use Gate described in the paper.

Data Format

For CoNLL-x format, the schema is: ID, FORM, LEMMA, CPOSTAG, POSTAG, MORPH-FEATURES, HEAD, DEPREL, PHEAD, PDEPREL

Running Experiments

1. Update ./examples/run_HPtrNetParser.sh to select the model you want to test, for example MODELNAME=HPtrNetPSTGate.
2. Run command bash ./examples/run_HPtrNetParser.sh.

Citation

Please cite our paper if you found the resources in this repository useful.

@inproceedings{liu2019hierarchical,
    title={Hierarchical Pointer Net Parsing},
    author={Linlin Liu and Xiang Lin and Shafiq Joty and Simeng Han and Lidong Bing},
    year={2019},
    month = {November},
    address={Hong Kong, China},
    url={https://arxiv.org/abs/1908.11571},
    booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing},
}