Target-oriented Proactive Dialogue Systems with Personalization: Problem Formulation and Dataset Curation

• URL: http://arxiv.org/abs/2310.07397v2
• Date: Fri, 13 Oct 2023 11:16:58 GMT
• Title: Target-oriented Proactive Dialogue Systems with Personalization: Problem Formulation and Dataset Curation
• Authors: Jian Wang, Yi Cheng, Dongding Lin, Chak Tou Leong, Wenjie Li
• Abstract summary: We explore a novel problem of personalized target-oriented dialogue by considering personalization during the target accomplishment process. 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.
• Score: 13.528753144870592
• License: http://creativecommons.org/licenses/by/4.0/
• 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 <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.

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