Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions
Mohammad Aliannejadi, Julia Kiseleva, Aleksandr Chuklin, Jeff Dalton, Mikhail Burtsev
摘要
Enabling open-domain dialogue systems to ask clarifying questions when appropriate is an important direction for improving the quality of the system response. Namely, for cases when a user request is not specific enough for a conversation system to provide an answer right away, it is desirable to ask a clarifying question to increase the chances of retrieving a satisfying answer. To address the problem of 'asking clarifying questions in opendomain dialogues': (1) we collect and release a new dataset focused on open-domain singleand multi-turn conversations, (2) we benchmark several state-of-the-art neural baselines, and (3) we propose a pipeline consisting of offline and online steps for evaluating the quality of clarifying questions in various dialogues. These contributions are suitable as a foundation for further research.
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引用它的顶会 Paper24
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它引用的顶会 Paper3
- ELECTRA: Pre-training Text Encoders as Discriminators Rather Than GeneratorsKevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. ManningICLR 2020 · 被引用 541 次
- Generating Clarifying Questions for Information RetrievalHamed Zamani, Susan T. Dumais, Nick Craswell, Paul N. Bennett 等WWW 2020 · 被引用 238 次
- Learning to Ask More: Semi-Autoregressive Sequential Question Generation under Dual-Graph InteractionZi Chai, Xiaojun WanACL 2020 · 被引用 26 次
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