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

Authors

43

  1. LW
    Ledell Wu

    Beijing Academy of Artificial Intelligence, China

    2 publications · 358 citations

    2

    h-index

  2. FP
    Fabio Petroni

    Meta (Israel), Israel

    2 publications · 358 citations

    2

    h-index

  3. SR
    Sebastian Riedel

    University College London, United Kingdom

    2 publications · 358 citations

    2

    h-index

  4. MJ
    Martin Josifoski

    École Polytechnique Fédérale de Lausanne, Switzerland

    2 publications · 358 citations

    2

    h-index

  5. LZ
    Luke Zettlemoyer

    University of Washington, United States

    2 publications · 358 citations

    2

    h-index

1–5 of 43

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 100 citations

    3

    h-index

  2. Technological Forecasting and Social Change

    1 publications · 16 citations

    1

    h-index

  3. Proceedings of the ACM Web Conference 2022

    Conference · 1 publications · 11 citations

    1

    h-index

1–3