Transformer-based Scoring

Transformer-Based Scoring is a method within automated essay scoring that employs transformer-based deep learning architectures to evaluate and assign scores to student essays by leveraging self-attention mechanisms for capturing contextual linguistic features.

48

Publications

4.1K

Citations

194

Authors

78

Institutions

Publications per year

2017–2026

48

Authors

194

  1. SH
    Samuel Humeau

    Meta (Israel), Israel

    2 publications · 324 citations

    2

    h-index

  2. NC
    Nick Craswell

    Microsoft (United States), United States

    2 publications · 43 citations

    2

    h-index

  3. ML
    Marie-Anne Lachaux

    Meta (Israel), Israel

    2 publications · 324 citations

    2

    h-index

  4. MT
    Masayuki Takamura

    Kanazawa University, Japan

    2 publications · 67 citations

    2

    h-index

  5. TE
    Takahide Etani

    Waseda University, Japan

    2 publications · 67 citations

    2

    h-index

1–5 of 194

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 2.6K citations

    6

    h-index

  2. Journal of Applied Psychology

    1 publications · 33 citations

    1

    h-index

  3. Computational Intelligence and Neuroscience

    1 publications · 27 citations

    1

    h-index

  4. IEEE Access

    1 publications · 65 citations

    1

    h-index

  5. 1

    h-index

1–5