  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.
