Target-oriented Proactive Dialogue Systems with Personalization: Problem Formulation and Dataset Curation

TL;DR

This paper introduces a new personalized target-oriented dialogue system framework, formulates the problem with a focus on personalization, and presents TopDial, a large-scale dataset created via an automatic curation method to advance research in this area.

Contribution

It formulates the personalized target-oriented dialogue problem and proposes an automatic dataset curation framework, resulting in the creation of the large-scale TopDial dataset.

Findings

TopDial contains about 18K dialogues demonstrating high quality.

The dataset effectively supports research in personalized target-oriented dialogue.

Experimental results validate the dataset's usefulness for advancing dialogue systems.

Abstract

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.