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
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.emnlp-main.344.pdf