Cross-lingual Representation Learning

Cross-Lingual Representation Learning is a methodological framework in multilingual natural language processing that enables the alignment and integration of semantic representations across different languages into a shared vector space, facilitating knowledge transfer and cross-lingual understanding.

438

Publications

28.1K

Citations

1.2K

Authors

317

Institutions

Publications per year

2017–2026

326

Authors

1.2K

  1. IV
    Ivan Vulić

    University of Cambridge, United Kingdom

    21 publications · 1.6K citations

    17

    h-index

  2. FW
    Furu Wei

    Microsoft Research Asia (China), China

    11 publications · 672 citations

    11

    h-index

  3. SR
    Sebastian Ruder

    Google (United States), United States

    11 publications · 1.5K citations

    11

    h-index

  4. MZ
    Ming Zhou

    Microsoft Research Asia (China), China

    10 publications · 1.1K citations

    10

    h-index

  5. WC
    Wanxiang Che

    Harbin Institute of Technology, China

    10 publications · 636 citations

    10

    h-index

1–5 of 1.2K

Venues

  1. arXiv (Cornell University)

    Repository · 67 publications · 3.5K citations

    29

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 14 publications · 524 citations

    13

    h-index

  3. 9

    h-index

  4. International Conference on Computational Linguistics

    Conference · 8 publications · 220 citations

    8

    h-index

  5. 6

    h-index

1–5