Complementing Lexical Retrieval with Semantic Residual Embedding
Luyu Gao, Zhuyun Dai, Chen, Tongfei, Zhen Fan, Van Durme, Benjamin, Jamie Callan · arXiv (Cornell University) · 2020
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.