@inproceedings{wang-etal-2023-target,
title = "Target-oriented Proactive Dialogue Systems with Personalization: Problem Formulation and Dataset Curation",
author = "Wang, Jian and
Cheng, Yi and
Lin, Dongding and
Leong, Chak and
Li, Wenjie",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.72",
doi = "10.18653/v1/2023.emnlp-main.72",
pages = "1132--1143",
abstract = "Target-oriented dialogue systems, designed to proactively steer conversations toward predefined targets or accomplish specific system-side goals, are an exciting area in conversational AI. In this work, by formulating a {\textless}dialogue act, topic{\textgreater} pair as the conversation target, we explore a novel problem of personalized target-oriented dialogue by considering personalization during the target accomplishment process. However, there remains an emergent need for high-quality datasets, and building one from scratch requires tremendous human effort. To address this, we propose an automatic dataset curation framework using a role-playing approach. Based on this framework, we construct a large-scale personalized target-oriented dialogue dataset, TopDial, which comprises about 18K multi-turn dialogues. The experimental results show that this dataset is of high quality and could contribute to exploring personalized target-oriented dialogue.",
}
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<abstract>Target-oriented dialogue systems, designed to proactively steer conversations toward predefined targets or accomplish specific system-side goals, are an exciting area in conversational AI. In this work, by formulating a \textlessdialogue act, topic\textgreater pair as the conversation target, we explore a novel problem of personalized target-oriented dialogue by considering personalization during the target accomplishment process. However, there remains an emergent need for high-quality datasets, and building one from scratch requires tremendous human effort. To address this, we propose an automatic dataset curation framework using a role-playing approach. Based on this framework, we construct a large-scale personalized target-oriented dialogue dataset, TopDial, which comprises about 18K multi-turn dialogues. The experimental results show that this dataset is of high quality and could contribute to exploring personalized target-oriented dialogue.</abstract>
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%0 Conference Proceedings
%T Target-oriented Proactive Dialogue Systems with Personalization: Problem Formulation and Dataset Curation
%A Wang, Jian
%A Cheng, Yi
%A Lin, Dongding
%A Leong, Chak
%A Li, Wenjie
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F wang-etal-2023-target
%X Target-oriented dialogue systems, designed to proactively steer conversations toward predefined targets or accomplish specific system-side goals, are an exciting area in conversational AI. In this work, by formulating a \textlessdialogue act, topic\textgreater pair as the conversation target, we explore a novel problem of personalized target-oriented dialogue by considering personalization during the target accomplishment process. However, there remains an emergent need for high-quality datasets, and building one from scratch requires tremendous human effort. To address this, we propose an automatic dataset curation framework using a role-playing approach. Based on this framework, we construct a large-scale personalized target-oriented dialogue dataset, TopDial, which comprises about 18K multi-turn dialogues. The experimental results show that this dataset is of high quality and could contribute to exploring personalized target-oriented dialogue.
%R 10.18653/v1/2023.emnlp-main.72
%U https://aclanthology.org/2023.emnlp-main.72
%U https://doi.org/10.18653/v1/2023.emnlp-main.72
%P 1132-1143
Markdown (Informal)
[Target-oriented Proactive Dialogue Systems with Personalization: Problem Formulation and Dataset Curation](https://aclanthology.org/2023.emnlp-main.72) (Wang et al., EMNLP 2023)
ACL