Cross-lingual Text Classification

Cross-Lingual Text Classification is a natural language processing task that involves categorizing textual content into predefined classes across different languages by leveraging cross-lingual representations or knowledge transfer, thereby enabling classification in a target language with limited or no labeled training data through its distinction from monolingual text classification.

375

Publications

21.1K

Citations

1.1K

Authors

376

Institutions

Publications per year

2017–2026

215

Authors

1.1K

  1. XW
    Xiaojun Wan

    Peking University, China

    12 publications · 1.1K citations

    12

    h-index

  2. GD
    Gerard de Melo

    Rutgers, The State University of New Jersey, United States

    7 publications · 309 citations

    7

    h-index

  3. KM
    Karl Moritz Hermann

    University of Oxford, United Kingdom

    5 publications · 536 citations

    5

    h-index

  4. IV
    Ivan Vulić

    University of Cambridge, United Kingdom

    5 publications · 258 citations

    5

    h-index

  5. DR
    Dan Roth

    University of Illinois Urbana-Champaign, United States

    5 publications · 342 citations

    5

    h-index

1–5 of 1.1K

Venues

  1. arXiv (Cornell University)

    Repository · 31 publications · 3.6K citations

    20

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 9 publications · 321 citations

    9

    h-index

  3. International Conference on Computational Linguistics

    Conference · 8 publications · 451 citations

    9

    h-index

  4. IEEE Access

    5 publications · 223 citations

    5

    h-index

  5. Information Processing & Management

    4 publications · 134 citations

    4

    h-index

1–5