@inproceedings{yang-etal-2021-xmoco,
title = "x{M}o{C}o: Cross Momentum Contrastive Learning for Open-Domain Question Answering",
author = "Yang, Nan and
Wei, Furu and
Jiao, Binxing and
Jiang, Daxing and
Yang, Linjun",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "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 = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.477",
doi = "10.18653/v1/2021.acl-long.477",
pages = "6120--6129",
abstract = "Dense passage retrieval has been shown to be an effective approach for information retrieval tasks such as open domain question answering. Under this paradigm, a dual-encoder model is learned to encode questions and passages separately into vector representations, and all the passage vectors are then pre-computed and indexed, which can be efficiently retrieved by vector space search during inference time. In this paper, we propose a new contrastive learning method called Cross Momentum Contrastive learning (xMoCo), for learning a dual-encoder model for question-passage matching. Our method efficiently maintains a large pool of negative samples like the original MoCo, and by jointly optimizing question-to-passage and passage-to-question matching tasks, enables using separate encoders for questions and passages. We evaluate our method on various open-domain question answering dataset, and the experimental results show the effectiveness of the proposed method.",
}
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%0 Conference Proceedings
%T xMoCo: Cross Momentum Contrastive Learning for Open-Domain Question Answering
%A Yang, Nan
%A Wei, Furu
%A Jiao, Binxing
%A Jiang, Daxing
%A Yang, Linjun
%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 yang-etal-2021-xmoco
%X Dense passage retrieval has been shown to be an effective approach for information retrieval tasks such as open domain question answering. Under this paradigm, a dual-encoder model is learned to encode questions and passages separately into vector representations, and all the passage vectors are then pre-computed and indexed, which can be efficiently retrieved by vector space search during inference time. In this paper, we propose a new contrastive learning method called Cross Momentum Contrastive learning (xMoCo), for learning a dual-encoder model for question-passage matching. Our method efficiently maintains a large pool of negative samples like the original MoCo, and by jointly optimizing question-to-passage and passage-to-question matching tasks, enables using separate encoders for questions and passages. We evaluate our method on various open-domain question answering dataset, and the experimental results show the effectiveness of the proposed method.
%R 10.18653/v1/2021.acl-long.477
%U https://aclanthology.org/2021.acl-long.477
%U https://doi.org/10.18653/v1/2021.acl-long.477
%P 6120-6129
Markdown (Informal)
[xMoCo: Cross Momentum Contrastive Learning for Open-Domain Question Answering](https://aclanthology.org/2021.acl-long.477) (Yang et al., ACL-IJCNLP 2021)
ACL
- Nan Yang, Furu Wei, Binxing Jiao, Daxing Jiang, and Linjun Yang. 2021. xMoCo: Cross Momentum Contrastive Learning for Open-Domain Question Answering. 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 6120–6129, Online. Association for Computational Linguistics.