Multi-Task Retrieval for Knowledge-Intensive Tasks

Jean Maillard, Vladimir Karpukhin, Fabio Petroni, Wen-tau Yih, Barlas Oguz, Veselin Stoyanov, Gargi Ghosh


Abstract
Retrieving relevant contexts from a large corpus is a crucial step for tasks such as open-domain question answering and fact checking. Although neural retrieval outperforms traditional methods like tf-idf and BM25, its performance degrades considerably when applied to out-of-domain data. Driven by the question of whether a neural retrieval model can be _universal_ and perform robustly on a wide variety of problems, we propose a multi-task trained model. Our approach not only outperforms previous methods in the few-shot setting, but also rivals specialised neural retrievers, even when in-domain training data is abundant. With the help of our retriever, we improve existing models for downstream tasks and closely match or improve the state of the art on multiple benchmarks.
Anthology ID:
2021.acl-long.89
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Editors:
Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1098–1111
Language:
URL:
https://aclanthology.org/2021.acl-long.89
DOI:
10.18653/v1/2021.acl-long.89
Bibkey:
Cite (ACL):
Jean Maillard, Vladimir Karpukhin, Fabio Petroni, Wen-tau Yih, Barlas Oguz, Veselin Stoyanov, and Gargi Ghosh. 2021. Multi-Task Retrieval for Knowledge-Intensive Tasks. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 1098–1111, Online. Association for Computational Linguistics.
Cite (Informal):
Multi-Task Retrieval for Knowledge-Intensive Tasks (Maillard et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2021.acl-long.89.pdf
Video:
 https://preview.aclanthology.org/ingest-acl-2023-videos/2021.acl-long.89.mp4
Data
FEVERKILTNatural QuestionsTriviaQA