论文标题
通过深度查询互动来学习多样的文档表示形式
Learning Diverse Document Representations with Deep Query Interactions for Dense Retrieval
论文作者
论文摘要
在本文中，我们提出了一个新的密集检索模型，该模型通过深度查询相互作用学习了各种文档表示。我们的模型用一组生成的伪Queries编码每个文档，以获取查询信息的多视文档表示。它不仅具有较高的推理效率，例如《香草双编码模型》，而且还可以在文档编码中启用深度查询文档的交互，并提供多方面的表示形式，以更好地匹配不同的查询。几个基准的实验证明了所提出的方法的有效性，表现优于强的双重编码基准。
In this paper, we propose a new dense retrieval model which learns diverse document representations with deep query interactions. Our model encodes each document with a set of generated pseudo-queries to get query-informed, multi-view document representations. It not only enjoys high inference efficiency like the vanilla dual-encoder models, but also enables deep query-document interactions in document encoding and provides multi-faceted representations to better match different queries. Experiments on several benchmarks demonstrate the effectiveness of the proposed method, out-performing strong dual encoder baselines.The code is available at \url{https://github.com/jordane95/dual-cross-encoder
