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. It has been argued that this is an inherent limitation of dense models. We disprove this claim by introducing the Salient Phrase Aware Retriever (SPAR), a dense retriever with the lexical matching capacity of a sparse model. In particular, we show that a dense retriever {\Lambda} can be trained to imitate a sparse one, and SPAR is built by augmenting a standard dense retriever with {\Lambda}. When evaluated on five open-domain question answering datasets and the MS MARCO passage retrieval task, SPAR sets a new state of the art for dense and sparse retrievers and can match or exceed the performance of more complicated dense-sparse hybrid systems.

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。

突出短语感知的密集检索：密集检索器是否能模仿稀疏检索器？