Offline Pseudo Relevance Feedback for Efficient and Effective Single-pass Dense Retrieval

Autor:	Wen, Xueru, Chen, Xiaoyang, Chen, Xuanang, He, Ben, Sun, Le
Rok vydání:	2023
Předmět:	Computer Science - Information Retrieval H.3.3
Druh dokumentu:	Working Paper
DOI:	10.1145/3539618.3592028
Popis:	Dense retrieval has made significant advancements in information retrieval (IR) by achieving high levels of effectiveness while maintaining online efficiency during a single-pass retrieval process. However, the application of pseudo relevance feedback (PRF) to further enhance retrieval effectiveness results in a doubling of online latency. To address this challenge, this paper presents a single-pass dense retrieval framework that shifts the PRF process offline through the utilization of pre-generated pseudo-queries. As a result, online retrieval is reduced to a single matching with the pseudo-queries, hence providing faster online retrieval. The effectiveness of the proposed approach is evaluated on the standard TREC DL and HARD datasets, and the results demonstrate its promise. Our code is openly available at https://github.com/Rosenberg37/OPRF. Comment: Accepted at SIGIR2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2308.10191 Zobrazit plný text záznamu View this record from Arxiv