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 <dialogue act, topic> 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.
@article{arxiv.2310.07397,
title = {Target-oriented Proactive Dialogue Systems with Personalization: Problem Formulation and Dataset Curation},
author = {Jian Wang and Yi Cheng and Dongding Lin and Chak Tou Leong and Wenjie Li},
journal= {arXiv preprint arXiv:2310.07397},
year = {2023}
}