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Title: Complement lexical retrieval model with semantic residual embeddings
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.  more » « less
Award ID(s):
1815528
PAR ID:
10273600
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
Advances in Information Retrieval – 43rd European Conference on IR Research
Page Range / eLocation ID:
146-160
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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