%0 Conference Proceedings %T Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision %A Sun, Si %A Qian, Yingzhuo %A Liu, Zhenghao %A Xiong, Chenyan %A Zhang, Kaitao %A Bao, Jie %A Liu, Zhiyuan %A Bennett, Paul %Y Zong, Chengqing %Y Xia, Fei %Y Li, Wenjie %Y Navigli, Roberto %S 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) %D 2021 %8 August %I Association for Computational Linguistics %C Online %F sun-etal-2021-shot %X 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. %R 10.18653/v1/2021.acl-long.390 %U https://aclanthology.org/2021.acl-long.390 %U https://doi.org/10.18653/v1/2021.acl-long.390 %P 5030-5043