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

RepBERT: Contextualized Text Embeddings for First-Stage Retrieval