  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.
