Learning Diverse Document Representations with Deep Query Interactions for Dense Retrieval - Li, Zehan; Yang, Nan; Wang, Liang; Wei, Furu (arXiv, 2022)
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 https://github.com/jordane95/dual-cross-encoder