DAM_STS

Reimplementation of the Decomposable Attention Model (DAM) for STS

If you use this software for academic research please cite the described paper:

@inproceedings{artetxe2018conll,
  author    = {Artetxe, Mikel  and  Labaka, Gorka  and Lopez-Gazpio, Inigo  and  Agirre, Eneko},
  title     = {Uncovering divergent linguistic information in word embeddings with lessons for intrinsic and extrinsic evaluation},
  booktitle = {Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018)},
  month     = {October},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics}
}

Requirements

• Python 2
• Python 3
• pyTorch (tested on 0.2)
• The following python modules: numpy, h5py and nltk.corpus.stopwords (optional)

Usage

1. Download and extract the STS Benchmark dataset from http://ixa2.si.ehu.es/stswiki/images/4/48/Stsbenchmark.tar.gz

wget http://ixa2.si.ehu.es/stswiki/images/4/48/Stsbenchmark.tar.gz tar xvzf Stsbenchmark.tar.gz

2. Process the dataset

python2 preprocess_datasets/process-STSBenchmark.py \
	--data_folder stsbenchmark \
	--out_folder stsbenchmark

3. Download and extract Glove word embeddings in the stsbenchmark folder from http://nlp.stanford.edu/data/glove.840B.300d.zip

wget http://nlp.stanford.edu/data/glove.840B.300d.zip
unzip glove.840B.300d.zip
mv glove.840B.300d.txt embeddings.glove.txt

4. Run Evaluate.sh script

./Evaluate.sh script

Acknowledgements

The project is motivated by the following papers and github repositories:

• A. Parikh, O. Täckström, D. Das, J. Uszkoreit, A decomposable attention model for natural language inference in Proceedings of the 2016 Conference on Empirical Methods in Natural Language Proceing, Association for Computational Linguistics, Austin, Texas, 2016, pp. 2249-2255. URL https://aclweb.org/anthology/D16-1244

• Decomposable Attention Model for Sentence Pair Classification https://github.com/harvardnlp/decomp-attn

• SNLI-decomposable-attention https://github.com/libowen2121/SNLI-decomposable-attention

• decomposable_attention https://github.com/shuuki4/decomposable_attention