Cross-encoder Ranking
Cross-Encoder Ranking is a relevance ranking approach that belongs to the class of neural reranking models, distinguished by its use of full interaction between a query and a document through a transformer-based architecture to produce a precise relevance score by jointly encoding their concatenated representations.
9
Publications
641
Citations
43
Authors
15
Institutions
Publications per year
2017–2026
9
Parents
9
Scalable Zero-shot Entity Linking with Dense Entity Retrieval
Ledell Wu, Fabio Petroni, Martin Josifoski et al. · 2020 · 338 citations · Full text
Large Dual Encoders Are Generalizable Retrievers
Jianmo Ni, Chen Qu, Jing Lü et al. · 2022 · 149 citations · Full text
Scalable Zero-shot Entity Linking with Dense Entity Retrieval
Ledell Wu, Fabio Petroni, Martin Josifoski et al. · arXiv (Cornell University) · 2019 · 20 citations · Full text
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University College London
London, United Kingdom
1 h-index
1 publications
338 citations
Apple (Germany)
Munich, Germany
1 h-index
11 publications
1.6K citations

Renmin University of China
Beijing, China
1 h-index
2 publications
30 citations

University of Electronic Science and Technology of China
Chengdu, China
1 h-index
2 publications
32 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
Meta (Israel)
Tel Aviv, Israel
1 h-index
4 publications
1.4K citations
École Polytechnique Fédérale de Lausanne
Lausanne, Switzerland
1 h-index
1 publications
338 citations
1–8 of 15
Repository · 3 publications · 100 citations
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1 publications · 16 citations
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Conference · 1 publications · 11 citations
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