Learning Diverse Document Representations With Deep Query Interactions For Dense Retrieval

Abstract

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

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• arxiv keyli2022learning

Code

• jordane95/dual-cross-encoder—

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