CoRT: Complementary Rankings from Transformers
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Recent approaches towards passage retrieval have successfully employed representations from pretrained Language Models(LMs) with large effectiveness gains. However, due to high computational cost those approaches are usually limited to re-ranking scenarios. The candidates in such a scenario are typically retrieved by scalable bag-of-words retrieval models such as BM25. Although BM25 has proven decent performance as a first-stage ranker, it tends to miss relevant passages. In this context we propose CoRT, a framework and neural first-stage ranking model that leverages contextual representations from transformer-based language models to complement candidates from term-based ranking functions while causing no significant delay. Using the MS MARCO dataset, we show that CoRT significantly increases first-stage ranking quality and recall by complementing BM25 with missing candidates. Consequently, we found subsequent re-rankers achieve superior results while requiring less candidates to saturate ranking quality. Finally, we demonstrate that with CoRT a representation-focused retrieval at web-scale can be realized with latencies as low as BM25.
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