Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering

Jiawei Zhou, Xiaoguang Li, Lifeng Shang, Lan Luo, Ke Zhan, Enrui Hu, Xinyu Zhang, Hao Jiang, Zhao Cao, Fan Yu, Xin Jiang, Qun Liu, Lei Chen


Abstract
To alleviate the data scarcity problem in training question answering systems, recent works propose additional intermediate pre-training for dense passage retrieval (DPR). However, there still remains a large discrepancy between the provided upstream signals and the downstream question-passage relevance, which leads to less improvement. To bridge this gap, we propose the HyperLink-induced Pre-training (HLP), a method to pre-train the dense retriever with the text relevance induced by hyperlink-based topology within Web documents. We demonstrate that the hyperlink-based structures of dual-link and co-mention can provide effective relevance signals for large-scale pre-training that better facilitate downstream passage retrieval. We investigate the effectiveness of our approach across a wide range of open-domain QA datasets under zero-shot, few-shot, multi-hop, and out-of-domain scenarios. The experiments show our HLP outperforms the BM25 by up to 7 points as well as other pre-training methods by more than 10 points in terms of top-20 retrieval accuracy under the zero-shot scenario. Furthermore, HLP significantly outperforms other pre-training methods under the other scenarios.
Anthology ID:
2022.acl-long.493
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7135–7146
Language:
URL:
https://aclanthology.org/2022.acl-long.493
DOI:
10.18653/v1/2022.acl-long.493
Bibkey:
Cite (ACL):
Jiawei Zhou, Xiaoguang Li, Lifeng Shang, Lan Luo, Ke Zhan, Enrui Hu, Xinyu Zhang, Hao Jiang, Zhao Cao, Fan Yu, Xin Jiang, Qun Liu, and Lei Chen. 2022. Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7135–7146, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering (Zhou et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.493.pdf
Software:
 2022.acl-long.493.software.zip
Code
 jzhoubu/hlp
Data
HotpotQAMS MARCONatural QuestionsTriviaQA