jtbates / paraphrastic-representations-at-scale

Paraphrastic Representations at Scale

Code to train models from "Paraphrastic Representations at Scale".

The code is written in Python 3.7 and requires H5py, jieba, numpy, scipy, sentencepiece, sacremoses, and PyTorch >= 1.0 libraries. These can be insalled with the following command:

pip install -r requirements.txt

To get started, download the data files used for training from http://www.cs.cmu.edu/~jwieting and download the STS evaluation data:

wget http://phontron.com/data/paraphrase-at-scale.zip
unzip paraphrase-at-scale.zip
rm paraphrase-at-scale.zip
wget http://www.cs.cmu.edu/~jwieting/STS.zip .
unzip STS.zip
rm STS.zip

If you use our code, models, or data for your work please cite:

@inproceedings{wieting19simple,
    title={Simple and Effective Paraphrastic Similarity from Parallel Translations},
    author={Wieting, John and Gimpel, Kevin and Neubig, Graham and Berg-Kirkpatrick, Taylor},
    booktitle={Proceedings of the Association for Computational Linguistics},
    url={https://arxiv.org/abs/1909.13872},
    year={2019}
}

To embed a list of sentences:

python embed_sentences.py --sentence-file paraphrase-at-scale/example-sentences.txt --load-file paraphrase-at-scale/model.para.lc.100.pt  --sp-model paraphrase-at-scale/paranmt.model --output-file sentence_embeds.np

To score a list of sentence pairs:

python score_sentence_pairs.py --sentence-pair-file paraphrase-at-scale/example-sentences-pairs.txt --load-file paraphrase-at-scale/model.para.lc.100.pt  --sp-model paraphrase-at-scale/paranmt.model

To download and preprocess raw data for training models (both bilingual and ParaNMT), see preprocess/bilingual and preprocess/paranmt.