The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.
Sparse, Dense, and Attentional Representations for Text Retrieval
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Abstract Dual encoders perform retrieval by encoding documents and queries into dense low-dimensional vectors, scoring each document by its inner product with the query. We investigate the capacity of this architecture relative to sparse bag-of-words models and attentional neural networks. Using both theoretical and empirical analysis, we establish connections between the encoding dimension, the margin between gold and lower-ranked documents, and the document length, suggesting limitations in the capacity of fixed-length encodings to support precise retrieval of long documents. Building on these insights, we propose a simple neural model that combines the efficiency of dual encoders with some of the expressiveness of more costly attentional architectures, and explore sparse-dense hybrids to capitalize on the precision of sparse retrieval. These models outperform strong alternatives in large-scale retrieval.
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“Based on our review of existing work, we identify two primary retrieval methods suitable for non-parametric memory including sparse methods based on bag-of-words models and dense methods based on deep representation learning for vector encoding and relevance score calculation [269, 270, 232, 271].”
From Human-inspired Perspectives: A Survey on AI Long-term Memory (2024) · cites this paper as [270]
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Self-citationBERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Qu, Y., Ding, Y., Liu, J., Liu, K., Ren, R., Zhao, W. X., Dong, D., Wu, H. R., & Wang, H. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.466 · Cited by 597
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
Xiong, L., Xiong, C., Li, Y., Tang, K., Liu, J., Bennett, P. N., Ahmed, J., & Overwikj, A. (2021) · International Conference on Learning Representations · Cited by 376
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Santhanam, K., Khattab, O., Saad-Falcon, J., Potts, C., & Zaharia, M. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.272 · Cited by 353
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Pretrained Transformers for Text Ranking: BERT and Beyond
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Metadata
- Authors
- Luan, Y., Eisenstein, J., Toutanova, K., & Collins, M.
- Affiliations
- Google (United States) (US)
- Corresponding author
- Luan, Yi
- Eisenstein, Jacob
- Toutanova, Kristina
- Collins, Michael
- Venue
- Transactions of the Association for Computational Linguistics
- Publisher
- MIT Press - Journals
- Published
- 2021
- Volume
- 9
- Pages
- 329-345
- ISSN
- 2307-387X (ISSN-L)
- OpenAlex
- W3157758108
- Cited by
- 363
- References
- 61
- Citations by year
- 2026: 46 · 2025: 48 · 2024: 39 · 2023: 81 · 2022: 69 · 2021: 61
- FWCI
- 56.52
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Transactions of the Association for Computational Linguistics (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- 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
- text retrieval, dual-encoder models, sparse retrieval, bag-of-words model, document length, retrieval precision, large-scale retrieval
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