A neural TurboSemanticParser as described in "Deep Multitask Learning for Semantic Dependency Parsing", Peng et al., ACL 2017.
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
MeurboParser
NeurboParser
deps
dynet @ 2f497b9
embedding Fix up some inconsistencies Nov 11, 2017
semeval2015_data Fix up some inconsistencies Nov 11, 2017
.gitmodules Fix up some inconsistencies Nov 11, 2017
AUTHORS
COPYING licences Aug 16, 2017
README.md Fix up some inconsistencies Nov 11, 2017
install_deps.sh

README.md

NeurboParser

This software implements the semantic dependency parsers described in [1]: './NeurboParser' corresponds to the basic model, and './MeurboParser' to the multitask models.

Required software

The following software are needed to build the parser:

Checking out the project for the first time

git clone https://github.com/Noahs-ARK/NeurboParser.git cd NeurboParser

The first time you clone the repository, you need to initialize the dynet/ submodule.

git submodule update --init

To fetch all the other required libs (except boost):

./install_deps.sh

To build the parser

mkdir -p NeurboParser/build
cd NeurboParser/build
cmake ..; make -j4
cd ../..

Running/training the parser

Follow the instructions under './semeval2015_data' to prepare the data. You can use the scripts in './NeurboParser' to train/evaluate the parser.

To replicate the results

You will first need to put 100-dimensional pretrained GloVe embedding [3] under 'embedding/'. Default hyperparameters in the scripts are used in [1].

The current version of DyNet uses some different strategies to deal with numerical issues than the older version we used in [1]. Based on our experience, we expect the current parser to have slightly better evaluation numbers on benchmark datasets than those described in [1].

We are still working on adapting the multitask models to use the new version of DyNet.

References

[1] Hao Peng, Sam Thomson, and Noah A. Smith. 2017. Deep Multitask Learning for Semantic Dependency Parsing In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL).

[2] André F. T. Martins, Miguel B. Almeida, Noah A. Smith. 2013. Turning on the Turbo: Fast Third-Order Non-Projective Turbo Parsers. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL).

[3] Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVe: Global Vectors for Word Representation. In Proceedings of the Empirical Methods in Natural Language Processing (EMNLP).

[4] André F. T. Martins and Mariana S. C. Almeida. 2014. Priberam: A Turbo Semantic Parser with Second Order Features. In Proceedubgs of the International Workshop on Semantic Evaluation (SemEval), task 8: Broad-Coverage Semantic Dependency Parsing.

[5] Mariana S. C. Almeida and André F. T. Martins. 2015. Lisbon: Evaluating TurboSemanticParser on Multiple Languages and Out-of-Domain Data. In Proceedings of International Workshop on Semantic Evaluation (SemEval'15), task 18: Broad Coverage Semantic Dependency Parsing.