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Complementing Lexical Retrieval With Semantic Residual Embedding

Luyu Gao, Zhuyun Dai, Tongfei Chen, Zhen Fan, Benjamin van Durme, Jamie Callan . Arxiv 2020 – 61 citations

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Compositional Generalization Efficiency Productivity Enhancement

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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