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.
RepBERT: Contextualized Text Embeddings for First-Stage Retrieval
RepBERT uses fixed-length contextualized embeddings for document and query representation, achieving state-of-the-art retrieval results on the MS MARCO Passage Ranking task with efficiency similar to bag-of-words methods.
- Year
- 2020
- Venue
- arXiv 2020
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- 5
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- Abstract onlyARXIV-DEFAULT
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- arxiv.org/abs/2006.15498v2ARXIV-DEFAULT
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