Low-resource Sequence Labeling

Low-Resource Sequence Labeling is a sequence labeling paradigm distinguished by the scarcity of annotated training data, often characterized by limited label availability, minimal supervision, or constrained linguistic resources, necessitating specialized techniques to achieve effective model performance under such constraints.

19

Publications

758

Citations

66

Authors

24

Institutions

Publications per year

2017–2026

18

Authors

66

  1. MA
    Michael A. Hedderich

    Saarland University, Germany

    3 publications · 53 citations

    3

    h-index

  2. JR
    Jason Riesa

    Google (United States), United States

    2 publications · 76 citations

    2

    h-index

  3. AS
    Aditya Siddhant

    Google (United States), United States

    2 publications · 76 citations

    2

    h-index

  4. DK
    Dietrich Klakow

    Saarland University, Germany

    2 publications · 34 citations

    2

    h-index

  5. KR
    Karthik Raman

    Indian Institute of Technology Madras, India

    2 publications · 76 citations

    2

    h-index

1–5 of 66

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 35 citations

    2

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 2 publications · 27 citations

    2

    h-index

  3. 1

    h-index

  4. 1

    h-index

  5. Computational Linguistics

    1 publications · 14 citations

    1

    h-index

1–5