vsuthichai / Paraphraser
Licence: mit
Sentence paraphrase generation at the sentence level
Stars: ✭ 283
Programming Languages
python
139335 projects - #7 most used programming language
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Paraphraser
This project providers users the ability to do paraphrase generation for sentences through a clean and simple API. A demo can be seen here: pair-a-phrase
The paraphraser was developed under the Insight Data Science Artificial Intelligence program.
Model
The underlying model is a bidirectional LSTM encoder and LSTM decoder with attention trained using Tensorflow. Downloadable link here: paraphrase model
Prerequisiteis
- python 3.5
- Tensorflow 1.4.1
- spacy
Inference Execution
Download the model checkpoint from the link above and run:
python inference.py --checkpoint=<checkpoint_path/model-171856>
Datasets
The dataset used to train this model is an aggregation of many different public datasets. To name a few:
- para-nmt-5m
- Quora question pair
- SNLI
- Semeval
- And more!
I have not included the aggregated dataset as part of this repo. If you're curious and would like to know more, contact me. Pretrained embeddings come from John Wieting's para-nmt-50m project.
Training
Training was done for 2 epochs on a Nvidia GTX 1080 and evaluted on the BLEU score. The Tensorboard training curves can be seen below. The grey curve is train and the orange curve is dev.
TODOs
- pip installable package
- Explore deeper number of layers
- Recurrent layer dropout
- Greater dataset augmentation
- Try residual layer
- Model compression
- Byte pair encoding for out of set vocabulary
Citations
@inproceedings { wieting-17-millions,
author = {John Wieting and Kevin Gimpel},
title = {Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations},
booktitle = {arXiv preprint arXiv:1711.05732}, year = {2017}
}
@inproceedings { wieting-17-backtrans,
author = {John Wieting, Jonathan Mallinson, and Kevin Gimpel},
title = {Learning Paraphrastic Sentence Embeddings from Back-Translated Bitext},
booktitle = {Proceedings of Empirical Methods in Natural Language Processing},
year = {2017}
}
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