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 candidatesto 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 .
Many recent approaches towards neural information retrieval mitigate their computational costs by using a multi-stage ranking pipeline. In the first stage, a number of potentially relevant candidates are retrieved using an efficient retrieval model 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 simple neural first-stage ranking model that leverages contextual representations from pretrained language models such as BERT to complement term-based ranking functions while causing no significant delay at query time . Using the MS MARCO dataset, we show that CoRT significantly increases the candidate recall by complementing BM25 with missing candidates. Consequently, we find subsequent re-rankers achieve superior results with less candidates. We further demonstrate that passage retrieval using CoRT can be realized with surprisingly low latencies .
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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
Many recent approaches towards neural information retrieval mitigate their computational costs by using a multi-stage ranking pipeline. In the first stage, a number of potentially relevant candidates are retrieved using an efficient retrieval model
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framework and
simple
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transformer-based language models to complement candidates from
pretrained language models such as BERT to complement
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at query time
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first-stage ranking quality and
the candidate
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found
find
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while requiring less candidatesto saturate ranking quality. Finally, we demonstrate that with CoRT a representation-focused retrieval at web-scale
with less candidates. We further demonstrate that passage retrieval using CoRT
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latencies as low as BM25
surprisingly low latencies