@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://preview.aclanthology.org/add-emnlp-2024-awards/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."
}