CoPrUS: Consistency Preserving Utterance Synthesis towards more realistic benchmark dialogues

TL;DR

This paper introduces CoPrUS, a method using large language models to generate realistic miscommunications in dialogue datasets, enhancing their diversity and realism for training more robust dialogue systems.

Contribution

It proposes a novel two-step LLM-based pipeline to create and repair miscommunications in dialogue datasets, addressing a gap in existing benchmark data.

Findings

LLMs can effectively generate realistic miscommunications.

The augmented dataset improves dialogue system robustness.

Nearly 1900 dialogues were modified and published as CoPrUS-MultiWOZ.

Abstract

Large-scale Wizard-Of-Oz dialogue datasets have enabled the training of deep learning-based dialogue systems. While they are successful as benchmark datasets, they lack certain types of utterances, which would make them more realistic. In this work, we investigate the creation of synthetic communication errors in an automatic pipeline. Based on linguistic theory, we propose and follow a simple error taxonomy. We focus on three types of miscommunications that could happen in real-world dialogues but are underrepresented in the benchmark dataset: misunderstandings, non-understandings and vaguely related questions. Our two-step approach uses a state-of-the-art Large Language Model (LLM) to first create the error and secondly the repairing utterance. We perform Language Model-based evaluation to ensure the quality of the generated utterances. We apply the method to the MultiWOZ dataset and evaluate it both qualitatively and empirically as well as with human judges. Our results indicate that current LLMs can aid in adding post-hoc miscommunications to benchmark datasets as a form of data augmentation. We publish the resulting dataset, in which nearly 1900 dialogues have been modified, as CoPrUS-MultiWOZ to facilitate future work on dialogue systems.