Pre-train a Discriminative Text Encoder for Dense Retrieval via Contrastive Span Prediction
Open accessAbstract
Dense retrieval has shown promising results in many information retrieval (IR) related tasks, whose foundation is high-quality text representation learning for effective search. Some recent studies have shown that autoencoder-based language models are able to boost the dense retrieval performance using a weak decoder. However, we argue that 1) it is not discriminative to decode all the input texts and, 2) even a weak decoder has the bypass effect on the encoder. Therefore, in this work, we introduce a novel contrastive span prediction task to pre-train the encoder alone, but still retain the bottleneck ability of the autoencoder. In this way, we can 1) learn discriminative text representations efficiently with the group-wise contrastive learning over spans and, 2) avoid the bypass effect of the decoder thoroughly. Comprehensive experiments over publicly available retrieval benchmark datasets show that our approach can outperform existing pre-training methods for dense retrieval significantly.
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Dense retrieval baselines generally perform better than sparse retrieval baselines on MS300K.
“The performance of dense retrieval baselines is generally better than that of sparse retrieval baselines, likely because the former uses dense vectors to capture richer semantic information, which is also consistent with the findings [31,32].”
From Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval (2025) · cites this paper as [32]
Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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“Other learning techniques like curriculum learning [1] are left for further studies to alleviate this problem.”
BackgroundDense passage retrieval for open-domain question answering
Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.550 · Cited by 3,575 · see citing works
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ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
Khattab, O., & Zaharia, M. (2020) · Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3397271.3401075 · Cited by 1,301 · see citing works
Latent retrieval for weakly supervised open domain question answering
Lee, K., Chang, M.-W., & Toutanova, K. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1612 · Cited by 771 · see citing works
Optimizing Dense Retrieval Model Training with Hard Negatives
Zhan, J., Mao, J., Liu, Y., Guo, J., Zhang, M., & Ma, S. (2021) · arXiv · doi:10.48550/arxiv.2104.08051 · Cited by 251 · see citing works
Self-citationOptimizing Dense Retrieval Model Training with Hard Negatives
Zhan, J., Mao, J., Liu, Y., Guo, J., Zhang, M., & Ma, S. (2021) · Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3404835.3462880 · Cited by 248 · see citing works
Self-citationOverview of the TREC 2020 Deep Learning Track
Craswell, N., Mitra, B., Yilmaz, E., & Campos, D. (2021) · arXiv · doi:10.48550/arxiv.2102.07662 · Cited by 107 · see citing works
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CorpusBrain: Pre-train a Generative Retrieval Model for Knowledge-Intensive Language Tasks
Chen, J., Zhang, R., Guo, J., Liu, Y., Fan, Y., & Cheng, X. (2022) · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · doi:10.1145/3511808.3557271 · Cited by 57 · see citing works
Self-citationContinual Learning for Generative Retrieval over Dynamic Corpora
Chen, J., Zhang, R., Guo, J., de Rijke, M., Chen, W., Fan, Y., & Cheng, X. (2023) · Proceedings of the 32nd ACM International Conference on Information and Knowledge Management · doi:10.1145/3583780.3614821 · Cited by 40 · see citing works
Self-citationLightweight and Direct Document Relevance Optimization for Generative Information Retrieval
Mekonnen, K. A., Tang, Y., & de Rijke, M. (2025) · Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3726302.3730023 · Cited by 3 · see citing works
“From Table 4, we can find: (i) On the MS MARCO dataset, removing pair-wise ranking optimization significantly reduces intermediate and broader recall metrics (R@5 and R@10) for both DDRO variants, highlighting its critical role in improving retrieval performance.”
- Authors
- Ma, X., Guo, J., Zhang, R., Fan, Y., & Cheng, X.
- Affiliations
- Institute of Computing Technology (CN)
- Venue
- Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
- Publisher
- ACM
- Published
- 2022-07-06
- Pages
- 848-858
- OpenAlex
- W4224865658
- Cited by
- 35
- References
- 9
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 10 · 2023: 15 · 2022: 4
- FWCI
- 7.60
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (No. 62006218 61902381 and 61872338)
- Lenovo-CAS Joint Lab Youth Scientist Project
- Youth Innovation Promotion Association CAS (No. 20144310 and 2021100)
- Foundation and Frontier Research Key Program of Chongqing Science and Technology Commission (No. cstc2017jcyjBX0059)
- 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, text representation learning
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