While entity-oriented neural IR models have advanced significantly, they
often overlook a key nuance: the varying degrees of influence individual
entities within a document have on its overall relevance. Addressing this gap,
we present DREQ, an entity-oriented dense document re-ranking model. Uniquely,
we emphasize the query-relevant entities within a document's representation
while simultaneously attenuating the less relevant ones, thus obtaining a
query-specific entity-centric document representation. We then combine this
entity-centric document representation with the text-centric representation of
the document to obtain a "hybrid" representation of the document. We learn a
relevance score for the document using this hybrid representation. Using four
large-scale benchmarks, we show that DREQ outperforms state-of-the-art neural
and non-neural re-ranking methods, highlighting the effectiveness of our
entity-oriented representation approach.

使用实体导向的稠密文件重新排序模型 (DREQ)，强调了文档中与查询相关的实体，并同时减弱了不相关的实体，从而获得了一个查询特定的实体中心的文档表示，并结合文档的文本中心表示，学习了文档的相关性分数，通过四个大规模基准测试表明 DREQ 胜过最先进的神经和非神经重新排序方法，突显了我们实体导向的表示方法的有效性。