Cross-encoder Retrieval Models
Cross-Encoder Retrieval Models is a retrieval model that, as a member of the neural retrieval models family, fundamentally differs from bi-encoder approaches by jointly encoding query and document in a cross-interaction manner to compute fine-grained semantic relevance scores through deep contextualized interaction.
13
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609
Citations
71
Authors
15
Institutions
Publications per year
2017–2026
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Large Dual Encoders Are Generalizable Retrievers
Jianmo Ni, Chen Qu, Jing Lü et al. · 2022 · 149 citations · Full text
Zero-shot Entity Linking with Dense Entity Retrieval.
Ledell Wu, Fabio Petroni, Martin Josifoski et al. · arXiv (Cornell University) · 2019 · 77 citations · Full text
Yohan Kim, Seongdeok Bang, Jiu Sohn et al. · Automation in Construction · 2021 · 61 citations
Adversarial Retriever-Ranker for dense text retrieval
Hang Zhang, Yeyun Gong, Yelong Shen et al. · arXiv (Cornell University) · 2021 · 37 citations · Full text
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University of Waterloo
Waterloo, Canada
2 h-index
7 publications
197 citations

University of Amsterdam
Amsterdam, The Netherlands
1 h-index
2 publications
24 citations
Amsterdam University of the Arts
Amsterdam, The Netherlands
1 h-index
2 publications
24 citations
Google (United States)
Mountain View, United States
1 h-index
11 publications
1.6K citations

University of Glasgow
Glasgow, United Kingdom
1 h-index
2 publications
54 citations
Yonsei University
Seoul, South Korea
1 h-index
3 publications
183 citations

Sun Yat-sen University
Guangzhou, China
1 h-index
1 publications
15 citations
Southwestern University of Finance and Economics
Chengdu, China
1 h-index
1 publications
16 citations
1–8 of 15
Repository · 4 publications · 156 citations
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TACL · 1 publications · 19 citations
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1 publications · 61 citations
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Conference · 1 publications · 27 citations
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1 publications · 16 citations
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