Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision

Si Sun, Yingzhuo Qian, Zhenghao Liu, Chenyan Xiong, Kaitao Zhang, Jie Bao, Zhiyuan Liu, Paul Bennett


Abstract
The effectiveness of Neural Information Retrieval (Neu-IR) often depends on a large scale of in-domain relevance training signals, which are not always available in real-world ranking scenarios. To democratize the benefits of Neu-IR, this paper presents MetaAdaptRank, a domain adaptive learning method that generalizes Neu-IR models from label-rich source domains to few-shot target domains. Drawing on source-domain massive relevance supervision, MetaAdaptRank contrastively synthesizes a large number of weak supervision signals for target domains and meta-learns to reweight these synthetic “weak” data based on their benefits to the target-domain ranking accuracy of Neu-IR models. Experiments on three TREC benchmarks in the web, news, and biomedical domains show that MetaAdaptRank significantly improves the few-shot ranking accuracy of Neu-IR models. Further analyses indicate that MetaAdaptRank thrives from both its contrastive weak data synthesis and meta-reweighted data selection. The code and data of this paper can be obtained from https://github.com/thunlp/MetaAdaptRank.
Anthology ID:
2021.acl-long.390
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
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5030–5043
Language:
URL:
https://aclanthology.org/2021.acl-long.390
DOI:
10.18653/v1/2021.acl-long.390
Bibkey:
Cite (ACL):
Si Sun, Yingzhuo Qian, Zhenghao Liu, Chenyan Xiong, Kaitao Zhang, Jie Bao, Zhiyuan Liu, and Paul Bennett. 2021. Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision. 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 5030–5043, Online. Association for Computational Linguistics.
Cite (Informal):
Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision (Sun et al., ACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.390.pdf
Video:
 https://aclanthology.org/2021.acl-long.390.mp4
Code
 thunlp/MetaAdaptRank
Data
MS MARCO