Knowledge Distillation For Mt

Knowledge Distillation for MT is a model compression technique in machine translation that transfers the learned linguistic and contextual knowledge from a large, high-performing teacher model to a smaller student model by mimicking its soft-label output distributions, thereby enabling efficient inference while preserving translation quality.

84

Publications

4.2K

Citations

326

Authors

96

Institutions

Publications per year

2017–2026

82

Authors

326

  1. JG
    Jiatao Gu

    Meta (Israel), Israel

    4 publications · 721 citations

    4

    h-index

  2. HW
    Haifeng Wang

    Baidu (China), China

    3 publications · 216 citations

    3

    h-index

  3. VO
    Victor O. K. Li

    University of Hong Kong, Hong Kong SAR China

    3 publications · 602 citations

    3

    h-index

  4. YF
    Yang Feng

    Chinese Academy of Sciences, China

    3 publications · 64 citations

    3

    h-index

  5. HW
    Hua Wu

    Baidu (China), China

    3 publications · 216 citations

    3

    h-index

1–5 of 326

Venues

  1. arXiv (Cornell University)

    Repository · 16 publications · 1.3K citations

    14

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 5 publications · 234 citations

    5

    h-index

  3. Pattern Recognition

    2 publications · 56 citations

    2

    h-index

  4. Information Sciences

    2 publications · 37 citations

    2

    h-index

  5. Neurocomputing

    2 publications · 55 citations

    2

    h-index

1–5