AISO

This repository hosts the authors' implementation of the paper Adaptive Information Seeking for Open-Domain Question Answering, published in EMNLP 2021.

Usage

Set up environment

Our experiments are conducted on Python 3.6 and PyTorch 1.4.

We employ GoldEn retriever as our query reformulator for the sparse retriever, so you need also install elasticsearch, java >= 8 and corenlp (run install_corenlp.sh).

Prepare index and training data

See index_sparse.ipynb, gen_step_data.ipynb.

Training

See train_union.py.

Inference

See game.ipynb.

Demo

The front-end of the demo in the first GIF is not open source, but we provide a simple visual interface based on jupyter widgets in the notebook.

TODO

• Convert jupyer notebooks to scripts
• More dependencies detail about environment setup
• Upload processed training data and model checkpoints

Citation

@inproceedings{zhu-etal-2021-adaptive,
    title = "Adaptive Information Seeking for Open-Domain Question Answering",
    author = "Zhu, Yunchang  and
      Pang, Liang  and
      Lan, Yanyan  and
      Shen, Huawei  and
      Cheng, Xueqi",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.293",
    pages = "3615--3626",
}