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In this paper, we propose a new dense retrieval model which learns diversedocument representations with deep query interactions. Our model encodes eachdocument with a set of generated pseudo-queries to get query-informed,multi-view document representations. It not only enjoys high inferenceefficiency like the vanilla dual-encoder models, but also enables deepquery-document interactions in document encoding and provides multi-facetedrepresentations to better match different queries. Experiments on severalbenchmarks demonstrate the effectiveness of the proposed method, out-performingstrong dual encoder baselines.The code is available at\url{https://github.com/jordane95/dual-cross-encoder

Learning Diverse Document Representations with Deep Query Interactions for Dense Retrieval