Task-Aware Specialization for Efficient and Robust Dense Retrieval for Open-Domain Question Answering
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).
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_smSee worksheets/01-in-domain-evaluation for steps to run in-domain evaluation experiments with TASER models.
More details coming soon!
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",
}