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.
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5.3K
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341
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118
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2017–2026
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Retrieval Augmentation Reduces Hallucination in Conversation
Kurt Shuster, Spencer Poff, Moya Chen et al. · 2021 · 465 citations · Full text
A Dataset for Research on Short-Text Conversations
Hao Wang, Zhengdong Lu, Hang Li et al. · 2013 · 191 citations · Full text
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Microsoft Research Asia (China)
Beijing, China
9 h-index
18 publications
1.5K citations

Peking University
Beijing, China
9 h-index
32 publications
1.8K citations
University of Science and Technology of China
Hefei, China
7 h-index
18 publications
1.4K citations

Beihang University
Beijing, China
5 h-index
12 publications
735 citations
Queen's University
Kingston, Canada
4 h-index
7 publications
419 citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
4 h-index
10 publications
560 citations
Microsoft (United States)
Redmond, United States
4 h-index
9 publications
742 citations
Carnegie Mellon University
Pittsburgh, United States
4 h-index
10 publications
218 citations
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Repository · 22 publications · 617 citations
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TASLP · 4 publications · 119 citations
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JASIS · 2 publications · 88 citations
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2 publications · 89 citations
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Conference · 2 publications · 135 citations
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