BotsTalk: Machine-sourced Framework for Automatic Curation of Large-scale Multi-skill Dialogue Datasets
Minju Kim, Chaehyeong Kim, Yongho Song, Seung-won Hwang, Jinyoung Yeo
摘要
To build open-domain chatbots that are able to use diverse communicative skills, we propose a novel framework BOTSTALK, where multiple agents grounded to the specific target skills participate in a conversation to automatically annotate multi-skill dialogues. We further present Blended Skill BotsTalk (BSBT), a large-scale multi-skill dialogue dataset comprising 300K conversations. Through extensive experiments, we demonstrate that our dataset can be effective for multi-skill dialogue systems which require an understanding of skill blending as well as skill grounding. Our code and data are available at https://github. com/convei-lab/BotsTalk .
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- Sequential Latent Knowledge Selection for Knowledge-Grounded DialogueByeongchang Kim, Jaewoo Ahn, Gunhee KimICLR 2020 · 被引用 179 次
- Knowledge-Grounded Dialogue Generation with Pre-trained Language ModelsXueliang Zhao, Wei Wu, Can Xu, Chongyang Tao 等EMNLP 2020 · 被引用 153 次
- Don't Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood TrainingMargaret Li, Stephen Roller, Ilia Kulikov, Sean Welleck 等ACL 2020 · 被引用 120 次
- Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-ConsciousnessHyunwoo Kim, Byeongchang Kim, Gunhee KimEMNLP 2020 · 被引用 46 次
- Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend SkillsEric Michael Smith, Mary Williamson, Kurt Shuster, Jason Weston 等ACL 2020 · 被引用 18 次
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