Exploiting Semantic and Syntactic Diversity for Diverse Task-oriented Dialogue

Abstract

Task-oriented dialogues have one-to-many property from semantic and syntactic perspectives, with many suitable dialogue acts and syntactic forms for a given post. However, current state-of-the-art task-oriented dialogue systems attempt to improve the quality of dialogues in terms of the most popular metrics (i.e., BLEU and entity F1), measuring the similarity between the generated responses and the human annotations, while the diversity of task-oriented dialogues remains less explored. This paper aims to improve the diversity of task-oriented dialogues from both semantic and syntactic perspectives by proposing a structural causal model to learn the causality composition of the dialogue acts and syntactic forms. Specifically, the disentangled understanding module decouples the dialogue into semantic and syntactic spaces and learns one-to-many property with multiple reference training. Then the casual collaboration generation module is proposed to apply Structural Causal Mechanism (SCM) to learn the causality composition relationship of the semantic and syntactic representations to generate the diverse response. Extensive experiments on the MultiWOZ datasets demonstrate that the proposed method achieves significantly better diversity than solid competitors.