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Cited by 248Information Retrieval and Search BehaviorTopic ModelingAdvanced Image and Video Retrieval Techniques

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Optimizing Dense Retrieval Model Training with Hard Negatives

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Zhan, Jingtao, Mao, Jiaxin, Liu, Yiqun, Guo, Jiafeng, Zhang, Min, Ma, Shaoping·2021·Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
DOI 10.1145/3404835.3462880
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Abstract

Ranking has always been one of the top concerns in information retrieval researches. For decades, the lexical matching signal has dominated the ad-hoc retrieval process, but solely using this signal in retrieval may cause the vocabulary mismatch problem. In recent years, with the development of representation learning techniques, many researchers turn to Dense Retrieval (DR) models for better ranking performance. Although several existing DR models have already obtained promising results, their performance improvement heavily relies on the sampling of training examples. Many effective sampling strategies are not efficient enough for practical usage, and for most of them, there still lacks theoretical analysis in how and why performance improvement happens. To shed light on these research questions, we theoretically investigate different training strategies for DR models and try to explain why hard negative sampling performs better than random sampling. Through the analysis, we also find that there are many potential risks in static hard negative sampling, which is employed by many existing training methods. Therefore, we propose two training strategies named a Stable Training Algorithm for dense Retrieval (STAR) and a query-side training Algorithm for Directly Optimizing Ranking pErformance (ADORE), respectively. STAR improves the stability of DR training process by introducing random negatives. ADORE replaces the widely-adopted static hard negative sampling method with a dynamic one to directly optimize the ranking performance. Experimental results on two publicly available retrieval benchmark datasets show that either strategy gains significant improvements over existing competitive baselines and a combination of them leads to the best performance.

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Metadata

Authors
Zhan, J., Mao, J., Liu, Y., Guo, J., Zhang, M., & Ma, S.
Affiliations
  • Tsinghua University (CN)
  • Renmin University of China (CN)
  • Institute of Computing Technology (CN)
ORCID
Venue
Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
Publisher
ACM
Published
2021-07-11
Pages
1503-1512
DOI
10.1145/3404835.3462880
OpenAlex
W3155895380
Semantic Scholar
7b577ba0e4230b2ac58d297b3d2cfc3d2f1aaace
Cited by
248
References
18
Citations by year
2026: 19 · 2025: 35 · 2024: 62 · 2023: 76 · 2022: 45 · 2021: 11
FWCI
40.15
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • Natural Science Foundation of China (61732008,61532011)
  • National Key Research and Development Program of China (2018YFC0831700)
  • Tsinghua University Guoqiang Research Institute
  • Beijing Academy of Artificial Intelligence
Topics
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
Keywords
dense retrieval, hard negative sampling, training strategies, ranking optimization, training stability, stars, ADORE, word matching, vocabulary mismatch, ad hoc retrieval

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