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microsoft/taser

Task-Aware Specialization for Efficient and Robust Dense Retrieval for Open-Domain Question Answering

License: MIT

This repository contains code and instructions for reproducing the experiments in the paper Task-Aware Specialization for Efficient and Robust Dense Retrieval for Open-Domain Question Answering (ACL 2023).

Approach Overview

Introduction (WIP)

git clone --recurse-submodules https://github.com/microsoft/taser

conda env create --file=environment.yml --name=taser

# activate the new sandbox you just created
conda activate taser
# add the `src/` and `third_party/DPR` to the list of places python searches for packages
conda develop src/ third_party/DPR/

# download spacy models
python -m spacy download en_core_web_sm

See worksheets/01-in-domain-evaluation for steps to run in-domain evaluation experiments with TASER models.

More details coming soon!

Citation

If you use any source code or data included in this repo, please cite our paper.

@inproceedings{cheng-etal-2023-task,
    title = "Task-Aware Specialization for Efficient and Robust Dense Retrieval for Open-Domain Question Answering",
    author = "Cheng, Hao  and
      Fang, Hao  and
      Liu, Xiaodong  and
      Gao, Jianfeng",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-short.159",
    pages = "1864--1875",
}

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