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arXiv · 2006.15498

RepBERT: Contextualized Text Embeddings for First-Stage Retrieval

Jingtao Zhan·Jiaxin Mao·Yiqun Liu·Min Zhang·Shaoping Ma
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Abstract

Although exact term match between queries and documents is the dominant method to perform first-stage retrieval, we propose a different approach, called RepBERT, to represent documents and queries with fixed-length contextualized embeddings. The inner products of query and document embeddings are regarded as relevance scores. On MS MARCO Passage Ranking task, RepBERT achieves state-of-the-art results among all initial retrieval techniques. And its efficiency is comparable to bag-of-words methods.

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Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma. 2020-07-20. RepBERT: Contextualized Text Embeddings for First-Stage Retrieval. https://arxiv.org/abs/2006.15498

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