Effective Contrastive Weighting for Dense Query Expansion

Wang, X., MacAvaney, S. , Macdonald, C. and Ounis, I. (2023) Effective Contrastive Weighting for Dense Query Expansion. In: 61st Annual Meeting of the Association for Computational Linguistics (ACL '23), Toronto, Canada, 9-14 July 2023, pp. 12688-12704. (doi: 10.18653/v1/2023.acl-long.710)

Abstract

Verbatim queries submitted to search engines often do not sufficiently describe the user’s search intent. Pseudo-relevance feedback (PRF) techniques, which modify a query’s representation using the top-ranked documents, have been shown to overcome such inadequacies and improve retrieval effectiveness for both lexical methods (e.g., BM25) and dense methods (e.g., ANCE, ColBERT). For instance, the recent ColBERT-PRF approach heuristically chooses new embeddings to add to the query representation using the inverse document frequency (IDF) of the underlying tokens. However, this heuristic potentially ignores the valuable context encoded by the embeddings. In this work, we present a contrastive solution that learns to select the most useful embeddings for expansion. More specifically, a deep language model-based contrastive weighting model, called CWPRF, is trained to learn to discriminate between relevant and non-relevant documents for semantic search. Our experimental results show that our contrastive weighting model can aid to select useful expansion embeddings and outperform various baselines. In particular, CWPRF can improve nDCG@10 by up to to 4.1% compared to an existing PRF approach for ColBERT while maintaining its efficiency.

• Item Type: Conference Proceedings
• Additional Information: Xiao Wang acknowledges support by the China Scholarship Council (CSC) from the Ministry of Education of P.R. China.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: MacAvaney, Dr Sean and Ounis, Professor Iadh and Macdonald, Professor Craig and Wang, Ms Xiao
• Authors: Wang, X., MacAvaney, S., Macdonald, C., and Ounis, I.
• College/School: College of Science and Engineering > School of Computing Science
• Copyright Holders: Copyright © 2023 Association for Computational Linguistics
• First Published: First published in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
• Publisher Policy: Reproduced under a Creative Commons licence

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