RepBERT: Contextualized Text Embeddings for First-Stage Retrieval
Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma · arXiv (Cornell University) · 2020
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.