Awesome

Stochastic Answer Networks for Machine Reading Comprehension

This PyTorch package implements the Stochastic Answer Network (SAN) for Machine Reading Comprehension, as described in:

Xiaodong Liu, Yelong Shen, Kevin Duh, Jianfeng Gao<br/> Stochastic Answer Networks for Machine Reading Comprehension<br/> Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)<br/> arXiv version

Xiaodong Liu, Wei Li, Yuwei Fang, Aerin Kim, Kevin Duh and Jianfeng Gao<br/> Stochastic Answer Networks for SQuAD 2.0 <br/> Technical Report arXiv version

Please cite the above papers if you use this code.

Quickstart

Setup Environment

1. python3.6
2. install requirements:

   pip install -r requirements.txt

3. download data/word2vec

   sh download.sh

4. You might need to download the en module for spacy

   python -m spacy download en # default English model (~50MB) <br/> python -m spacy download en_core_web_md # larger English model (~1GB)

Or pull our published docker: allenlao/pytorch-allennlp-rt

Train a SAN Model on SQuAD v1.1

1. preprocess data

   python prepro.py

2. train a model

   python train.py

Train a SAN Model on SQuAD v2.0

1. preprocess data

   python prepro.py --v2_on

2. train a Model

   python train.py --v2_on --dev_gold data\dev-v2.0.json

Use of ELMo

1. download ELMo resource from AllenNLP
2. train a Model with ELMo

   python train.py --elmo_on

Note that we only tested on SQuAD v1.1.

TODO

1. Multi-Task Training.
2. Add BERT.

Notes and Acknowledgments

Some of code are adapted from: https://github.com/hitvoice/DrQA <br/> ELMo is from: https://allennlp.org

Results

We report results produced by this package as follows.

Related:

1. <a href="https://arxiv.org/abs/1809.06963">Multi-Task Learning for MRC</a>
2. <a href="https://arxiv.org/abs/1804.07888">NLI</a>