Despite their recent popularity and well-known advantages, dense retrievers
still lag behind sparse methods such as BM25 in their ability to reliably match
salient phrases and rare entities in the query and to generalize to
out-of-domain data. It has been argued that this is an inherent limitation of
dense models. We rebut this claim by introducing the Salient Phrase Aware
Retriever (SPAR), a dense retriever with the lexical matching capacity of a
sparse model. We show that a dense Lexical Model {\Lambda} can be trained to
imitate a sparse one, and SPAR is built by augmenting a standard dense
retriever with {\Lambda}. Empirically, SPAR shows superior performance on a
range of tasks including five question answering datasets, MS MARCO passage
retrieval, as well as the EntityQuestions and BEIR benchmarks for out-of-domain
evaluation, exceeding the performance of state-of-the-art dense and sparse
retrievers. The code and models of SPAR are available at:
this https URL

介绍了一种称为 Salient Phrase Aware Retriever (SPAR) 的密集型检索器，在保留稀疏模型的词法匹配能力的同时，通过加入类似于稀疏模型的词汇模型 Lambda 来提高其检索性能。SPAR 在多个任务上表现出卓越性能，包括问答数据集，MS MARCO 段落检索以及 EntityQuestions 和 BEIR 评估。