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Disentangled Modeling of Domain and Relevance for Adaptable Dense Retrieval. (arXiv:2208.05753v1 [cs.IR] CROSS LISTED)
Aug. 18, 2022, 1:11 a.m. | Jingtao Zhan, Qingyao Ai, Yiqun Liu, Jiaxin Mao, Xiaohui Xie, Min Zhang, Shaoping Ma
cs.CL updates on arXiv.org arxiv.org
Recent advance in Dense Retrieval (DR) techniques has significantly improved
the effectiveness of first-stage retrieval. Trained with large-scale supervised
data, DR models can encode queries and documents into a low-dimensional dense
space and conduct effective semantic matching. However, previous studies have
shown that the effectiveness of DR models would drop by a large margin when the
trained DR models are adopted in a target domain that is different from the
domain of the labeled data. One of the possible reasons …
More from arxiv.org / cs.CL updates on arXiv.org
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