BotsTalk: Machine-sourced Framework for Automatic Curation of Large-scale Multi-skill Dialogue Datasets

Minju Kim, Chaehyeong Kim, Yong Ho Song, Seung-won Hwang, Jinyoung Yeo


Abstract
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.
Anthology ID:
2022.emnlp-main.344
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5149–5170
Language:
URL:
https://aclanthology.org/2022.emnlp-main.344
DOI:
10.18653/v1/2022.emnlp-main.344
Bibkey:
Cite (ACL):
Minju Kim, Chaehyeong Kim, Yong Ho Song, Seung-won Hwang, and Jinyoung Yeo. 2022. BotsTalk: Machine-sourced Framework for Automatic Curation of Large-scale Multi-skill Dialogue Datasets. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 5149–5170, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
BotsTalk: Machine-sourced Framework for Automatic Curation of Large-scale Multi-skill Dialogue Datasets (Kim et al., EMNLP 2022)
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PDF:
https://aclanthology.org/2022.emnlp-main.344.pdf