  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.
