Clarification Question Generation

Clarification Question Generation is a computational process in dialogue systems that involves formulating targeted questions to resolve ambiguities or elicit missing information when user intent or input is insufficiently specified.

24

Publications

726

Citations

75

Authors

38

Institutions

Publications per year

2017–2026

16

Authors

75

  1. MP
    Matthew Purver

    Queen Mary University of London, United Kingdom

    2 publications · 93 citations

    2

    h-index

  2. MA
    Mohammad Aliannejadi

    University of Amsterdam, Netherlands

    2 publications · 69 citations

    2

    h-index

  3. IS
    Ivan Sekulić

    Università della Svizzera italiana, Switzerland

    1 publications · 37 citations

    1

    h-index

  4. NC
    Nick Craswell

    Microsoft (United States), United States

    1 publications · 14 citations

    1

    h-index

  5. WL
    Wenjie Li

    Hong Kong Polytechnic University, Hong Kong SAR China

    1 publications · 20 citations

    1

    h-index

1–5 of 75

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 87 citations

    3

    h-index

  2. 1

    h-index

  3. 1

    h-index

  4. Information Sciences

    1 publications · 18 citations

    1

    h-index

  5. 1

    h-index

1–5