Neural Machine Translation

Neural Machine Translation is a machine translation approach that employs deep neural networks to model and generate natural language translations in an end-to-end manner, distinguishing itself from traditional statistical or rule-based methods through its use of continuous representations and learned contextual embeddings.

2.3K

Publications

303.6K

Citations

4.1K

Authors

918

Institutions

Publications per year

2017–2026

2K

Authors

4.1K

  1. TQ
    Tao Qin

    Microsoft Research Asia (China), China

    50 publications · 5.7K citations

    34

    h-index

  2. RS
    Rico Sennrich

    University of Edinburgh, United Kingdom

    55 publications · 5.9K citations

    34

    h-index

  3. GN
    Graham Neubig

    Carnegie Mellon University, United States

    55 publications · 3.5K citations

    31

    h-index

  4. ZT
    Zhaopeng Tu

    Tencent (China), China

    53 publications · 3.2K citations

    28

    h-index

  5. JG
    Jiatao Gu

    Meta (Israel), Israel

    37 publications · 4.8K citations

    27

    h-index

1–5 of 4.1K

Venues

  1. arXiv (Cornell University)

    Repository · 513 publications · 75.2K citations

    89

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 78 publications · 5.6K citations

    39

    h-index

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

    TASLP · 28 publications · 954 citations

    21

    h-index

  4. Transactions of the Association for Computational Linguistics

    TACL · 18 publications · 4.5K citations

    18

    h-index

  5. IEEE Access

    18 publications · 822 citations

    17

    h-index

1–5