Retrieval-based Response Generation

Retrieval-Based Response Generation is a response generation paradigm within dialogue systems that selects and returns a pre-existing response from a fixed repository of candidate responses based on semantic similarity and contextual relevance, rather than generating novel textual output.

98

Publications

5.3K

Citations

341

Authors

118

Institutions

Publications per year

2017–2026

88

Authors

341

  1. RY
    Rui Yan

    Peking University, China

    10 publications · 921 citations

    10

    h-index

  2. WW
    Wei Wu

    Beihang University, China

    7 publications · 512 citations

    7

    h-index

  3. CT
    Chongyang Tao

    Peking University, China

    7 publications · 410 citations

    7

    h-index

  4. ZL
    Zhen-Hua Ling

    University of Science and Technology of China, China

    6 publications · 343 citations

    6

    h-index

  5. DZ
    Dongyan Zhao

    Peking University, China

    6 publications · 485 citations

    6

    h-index

1–5 of 341

Venues

  1. arXiv (Cornell University)

    Repository · 22 publications · 617 citations

    16

    h-index

  2. 4

    h-index

  3. Journal of the American Society for Information Science

    JASIS · 2 publications · 88 citations

    2

    h-index

  4. Computational Linguistics

    2 publications · 89 citations

    2

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 2 publications · 135 citations

    2

    h-index

1–5