Target-oriented Proactive Dialogue Systems with Personalization: Problem Formulation and Dataset Curation
This work addresses the need for datasets in conversational AI for personalized target-oriented dialogue, but it is incremental as it focuses on dataset creation rather than novel methods.
The paper tackles the lack of high-quality datasets for personalized target-oriented dialogue systems by proposing an automatic curation framework, resulting in the creation of TopDial, a dataset with about 18K multi-turn dialogues that is shown to be high-quality.
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.