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.

翻译：面向目标的对话系统旨在主动引导对话朝着预设目标或完成特定系统侧任务发展，是对话式人工智能中一个令人兴奋的研究领域。本文通过将<对话行为，主题>二元组定义为对话目标，探索了在目标达成过程中考虑个性化因素的个性化面向目标对话这一新问题。然而，当前仍迫切需要高质量数据集，而从头构建此类数据集需要大量人力投入。为此，我们提出了一种基于角色扮演的自动化数据集构建框架。基于该框架，我们构建了大规模个性化面向目标对话数据集TopDial，包含约1.8万轮多轮对话。实验结果表明，该数据集质量优良，能够为探索个性化面向目标对话研究提供有力支撑。