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.
@article{arxiv.2210.12687,
title = {BotsTalk: Machine-sourced Framework for Automatic Curation of Large-scale Multi-skill Dialogue Datasets},
author = {Minju Kim and Chaehyeong Kim and Yongho Song and Seung-won Hwang and Jinyoung Yeo},
journal= {arXiv preprint arXiv:2210.12687},
year = {2022}
}
Comments
Accepted to EMNLP2022. Code and data are available at https://github.com/convei-lab/BotsTalk