Unsupervised Machine Translation

Unsupervised Machine Translation is a machine translation approach that belongs to the class of neural machine translation methods and is distinguished by its ability to learn translation models without relying on parallel corpora, instead leveraging monolingual data and unsupervised learning techniques to induce cross-lingual correspondences.

296

Publications

18.6K

Citations

819

Authors

245

Institutions

Publications per year

2017–2026

201

Authors

819

  1. GL
    Guillaume Lample

    Meta (Israel), Israel

    9 publications · 2.9K citations

    9

    h-index

  2. GN
    Graham Neubig

    Carnegie Mellon University, United States

    8 publications · 480 citations

    8

    h-index

  3. ES
    Eiichiro Sumita

    National Institute of Information and Communications Technology, Japan

    8 publications · 306 citations

    8

    h-index

  4. TQ
    Tao Qin

    Microsoft Research Asia (China), China

    7 publications · 1.5K citations

    7

    h-index

  5. SL
    Shujie Liu

    Microsoft Research Asia (China), China

    7 publications · 280 citations

    7

    h-index

1–5 of 819

Venues

  1. arXiv (Cornell University)

    Repository · 48 publications · 5.6K citations

    23

    h-index

  2. Neural Information Processing Systems

    Conference · 5 publications · 367 citations

    5

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 4 publications · 182 citations

    4

    h-index

  4. Computational Linguistics

    3 publications · 112 citations

    3

    h-index

  5. 3

    h-index

1–5