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：目标导向对话系统旨在主动引导对话走向预设目标或完成特定系统侧任务，是对话式AI领域的前沿方向。本文通过定义<对话行为，话题>二元组作为对话目标，探索了在目标达成过程中融入个性化要素的新型个性化目标导向对话问题。然而当前仍存在高质量数据集匮乏的瓶颈，人工构建此类数据集需要耗费巨大精力。为此，我们提出采用角色扮演方法的自动数据集构建框架。基于该框架，我们构建了包含约1.8万轮多轮对话的大规模个性化目标导向对话数据集TopDial。实验结果表明，该数据集具有高质量特性，可为个性化目标导向对话研究提供有力支撑。