Cited by 394Information Retrieval and Search BehaviorTopic ModelingAdvanced Image and Video Retrieval Techniques

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Sparse, Dense, and Attentional Representations for Text Retrieval

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Luan, Yi, Eisenstein, Jacob, Toutanova, Kristina, Collins, Michael·2021·Transactions of the Association for Computational Linguistics

DOI 10.1162/tacl_a_00369

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Abstract

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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Authors: Luan, Y., Eisenstein, J., Toutanova, K., & Collins, M.
Affiliations: Google (United States) (US)
Corresponding author: Luan, Yi
Eisenstein, Jacob
Toutanova, Kristina
Collins, Michael
ORCID: 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)
DOI: 10.1162/tacl_a_00369
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
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor, 117 d)
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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Sparse, Dense, and Attentional Representations for Text Retrieval

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