TY - GEN
T1 - Target-oriented Proactive Dialogue Systems with Personalization: Problem Formulation and Dataset Curation
AU - Wang, Jian
AU - Cheng, Yi
AU - Lin, Dongding
AU - Leong, Chak Tou
AU - Li, Wenjie
N1 - Publisher Copyright:
©2023 Association for Computational Linguistics.
PY - 2023/12
Y1 - 2023/12
N2 - 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 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, TOPDIAL1, 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.
AB - 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 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, TOPDIAL1, 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.
UR - http://www.scopus.com/inward/record.url?scp=85184820016&partnerID=8YFLogxK
M3 - Conference article published in proceeding or book
AN - SCOPUS:85184820016
T3 - EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings
SP - 1132
EP - 1143
BT - EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings
A2 - Bouamor, Houda
A2 - Pino, Juan
A2 - Bali, Kalika
PB - Association for Computational Linguistics (ACL)
T2 - 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023
Y2 - 6 December 2023 through 10 December 2023
ER -