IMSnPars

IMS Neural Dependency Parser is a re-implementation of the transition- and graph-based parsers described in Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations

The parser was developed for the paper The (Non-)Utility of Structural Features in BiLSTM-based Dependency Parsers (see acl2019 branch for all the paper specific changes and analysis tools):

@inproceedings{falenska-kuhn-2019-non,
    title = "The (Non-)Utility of Structural Features in {B}i{LSTM}-based Dependency Parsers",
    author = "Falenska, Agnieszka  and Kuhn, Jonas",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-1012",
    doi = "10.18653/v1/P19-1012",
    pages = "117--128",
}

Required software

Python 3.7

Dynet 2.0, see its installation instructions for compile-time requirements

NetworkX package

Usage

Transition-based parser

Training a new model:

python3 imsnpars/main.py --parser TRANS --train [train_file] --save [model_file]

Loading and predicting with the trained model:

python3 imsnpars/main.py --parser TRANS --model [model_file] --test  [test_file] --output [output_file]

The parser supports many other options. All of them can be seen after running:

python3 imsnpars/main.py --parser TRANS --help

Graph-based parser

Training a new model:

python3 imsnpars/main.py --parser GRAPH --train [train_file] --save [model_file]

Loading and predicting with the trained model:

python3 imsnpars/main.py --parser GRAPH --model [model_file] --test  [test_file] --output [output_file]

The parser supports many other options. All of them can be seen after running:

python3 imsnpars/main.py --parser GRAPH --help

Tests

IMSnPars comes with four testing scripts to check if everything works fine:

1. systests/test_trans_parser.sh -- trains a new transition-based parser on small fake data and uses this model for prediction
2. systests/test_graph_parser.sh -- trains a new graph-based parser on small fake data and uses this model for prediction
3. systests/test_all_trans_parsers.sh -- trains multiple transition-based models with different sets of options
4. systests/test_all_graph_parsers.sh -- trains multiple graph-based models with different sets of options

We recommend running the two first scripts before using IMSnPars for other purposes (both tests take less than a minute). Both of the scripts should end with an information that everything went fine. Transition-based parser achieves LAS=64.61 on the fake data and the graph-based one LAS=66.47.