/qait_public

Question Answering with Interactive Text (QAit), code for EMNLP 2019 paper "Interactive Language Learning by Question Answering"

Primary LanguagePythonMIT LicenseMIT

Interactive Language Learning by Question Answering


Code for EMNLP 2019 paper "Interactive Language Learning by Question Answering".

To install dependencies

sudo apt update
conda create -p ~/venvs/qait python=3.6
source activate ~/venvs/qait
pip install --upgrade pip
pip install numpy==1.16.4
pip install https://github.com/Microsoft/TextWorld/archive/rebased-interactive-qa.zip
pip install -U spacy
python -m spacy download en
pip install tqdm h5py visdom pyyaml
conda install pytorch torchvision cudatoolkit=9.2 -c pytorch

Test Set

Download the test set from https://aka.ms/qait-testset. Unzip it.

Pretrained Word Embeddings

Before first time running it, download fasttext crawl-300d-2M.vec.zip from HERE, unzip, and run embedding2h5.py for fast embedding loading in the future.

To Train

python train.py ./

Citation

Please use the following bibtex entry:

@article{yuan2019qait,
  title={Interactive Language Learning by Question Answering},
  author={Yuan, Xingdi and C\^ot\'{e}, Marc-Alexandre and Fu, Jie and Lin, Zhouhan and Pal, Christopher and Bengio, Yoshua and Trischler, Adam},
  booktitle={EMNLP},
  year={2019}
}

License

MIT