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

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).

See index_sparse.ipynb, gen_step_data.ipynb.

See train_union.py.

See game.ipynb.

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.

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

@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",
}