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Dual Skipping Guidance for Document Retrieval with Learned Sparse Representations ...
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Composite Re-Ranking for Efficient Document Search with BERT ...
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Abstract:
Although considerable efforts have been devoted to transformer-based ranking models for document search, the relevance-efficiency tradeoff remains a critical problem for ad-hoc ranking. To overcome this challenge, this paper presents BECR (BERT-based Composite Re-Ranking), a composite re-ranking scheme that combines deep contextual token interactions and traditional lexical term-matching features. In particular, BECR exploits a token encoding mechanism to decompose the query representations into pre-computable uni-grams and skip-n-grams. By applying token encoding on top of a dual-encoder architecture, BECR separates the attentions between a query and a document while capturing the contextual semantics of a query. In contrast to previous approaches, this framework does not perform expensive BERT computations during online inference. Thus, it is significantly faster, yet still able to achieve high competitiveness in ad-hoc ranking relevance. Finally, an in-depth comparison between BECR and other ... : to be published in WSDM'22 ...
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Keyword:
FOS Computer and information sciences; Information Retrieval cs.IR
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URL: https://arxiv.org/abs/2103.06499 https://dx.doi.org/10.48550/arxiv.2103.06499
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