Learning Diverse Document Representations with Deep Query Interactions for Dense Retrieval
Information Retrieval
2022-08-09 v1 Computation and Language
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
Cite
@article{arxiv.2208.04232,
title = {Learning Diverse Document Representations with Deep Query Interactions for Dense Retrieval},
author = {Zehan Li and Nan Yang and Liang Wang and Furu Wei},
journal= {arXiv preprint arXiv:2208.04232},
year = {2022}
}