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 query-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, enables using separate encoders for questions and passages. We evaluate our method on various open domain QA datasets, and the experimental results show the effectiveness of the proposed approach.
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