@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://preview.aclanthology.org/fix-sig-urls/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 {\ensuremath{<}}dialogue act, topic{\ensuremath{>}} 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."
}