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arXiv · 2004.13969

Complementing Lexical Retrieval with Semantic Residual Embedding

Luyu Gao·Zhuyun Dai·Tongfei Chen·Zhen Fan·Benjamin Van Durme·Jamie Callan
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Abstract

This paper presents CLEAR, a retrieval model that seeks to complement classical lexical exact-match models such as BM25 with semantic matching signals from a neural embedding matching model. CLEAR explicitly trains the neural embedding to encode language structures and semantics that lexical retrieval fails to capture with a novel residual-based embedding learning method. Empirical evaluations demonstrate the advantages of CLEAR over state-of-the-art retrieval models, and that it can substantially improve the end-to-end accuracy and efficiency of reranking pipelines.

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Luyu Gao, Zhuyun Dai, Tongfei Chen, Zhen Fan, Benjamin Van Durme, Jamie Callan. 2020-04-29. Complementing Lexical Retrieval with Semantic Residual Embedding. https://arxiv.org/abs/2004.13969

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