面向目标的个性化主动对话系统：问题形式化与数据集构建
Target-oriented Proactive Dialogue Systems with Personalization: Problem Formulation and Dataset Curation
AI总结：
本文提出个性化面向目标对话问题，将<对话行为，主题>作为目标，并基于角色扮演方法构建大规模数据集TopDial（约18K轮），实验证明其高质量，为相关研究提供支撑。
摘要：
面向目标的对话系统旨在主动将对话引导至预定义目标或完成特定的系统侧目标，是对话式AI中一个令人兴奋的研究领域。在本工作中，通过将<对话行为，主题>对形式化为对话目标，我们探索了一个新颖的个性化面向目标对话问题，即在目标实现过程中考虑个性化因素。然而，目前对高质量数据集的需求日益迫切，而从头构建此类数据集需要大量人力。为解决这一问题，我们提出了一种基于角色扮演方法的自动数据集构建框架。基于该框架，我们构建了一个大规模个性化面向目标对话数据集TopDial，包含约18K轮多轮对话。实验结果表明，该数据集质量高，有助于探索个性化面向目标对话。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
