EMNLP 2022main16 citations
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.
BibTeX
@inproceedings{kim-etal-2022-botstalk,
title = "{B}ots{T}alk: Machine-sourced Framework for Automatic Curation of Large-scale Multi-skill Dialogue Datasets",
author = "Kim, Minju and
Kim, Chaehyeong and
Song, Yong Ho and
Hwang, Seung-won and
Yeo, Jinyoung",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.344/",
doi = "10.18653/v1/2022.emnlp-main.344",
pages = "5149--5170"
}