Cross-lingual Transfer

Cross-Lingual Transfer is a machine translation paradigm in which knowledge acquired during training on a source language is systematically leveraged to improve performance in a target language, thereby enabling the effective adaptation of linguistic representations across different languages through transfer learning mechanisms.

953

Publications

56K

Citations

2.5K

Authors

565

Institutions

Publications per year

2017–2026

682

Authors

2.5K

  1. IV
    Ivan Vulić

    University of Cambridge, United Kingdom

    27 publications · 2K citations

    20

    h-index

  2. GG
    Goran Glavašš

    University of Mannheim, Germany

    19 publications · 851 citations

    15

    h-index

  3. FW
    Furu Wei

    Microsoft Research Asia (China), China

    19 publications · 970 citations

    15

    h-index

  4. GN
    Graham Neubig

    Carnegie Mellon University, United States

    18 publications · 1.4K citations

    14

    h-index

  5. AK
    Anna Korhonen

    University of Cambridge, United Kingdom

    13 publications · 679 citations

    13

    h-index

1–5 of 2.5K

Venues

  1. arXiv (Cornell University)

    Repository · 147 publications · 8.8K citations

    39

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 33 publications · 2.1K citations

    21

    h-index

  3. 15

    h-index

  4. 14

    h-index

  5. IEEE/ACM Transactions on Audio Speech and Language Processing

    TASLP · 11 publications · 256 citations

    11

    h-index

1–5