Towards Cost-effective Multi-style Conversations: A Pilot Study in Task-oriented Dialogue Generation

Abstract

Conversations exhibit significant variation when different styles are employed by participants, often leading to subpar performance when a dialogue model is exclusively trained on single-style datasets. We present a cost-effective methodology for generating multi-style conversations, which can be used in the development of conversational agents. This methodology only assumes the availability of a conversational domain, such as a knowledge base, and leverages the generative capabilities of large language models. In a pilot study focused on the generation aspect of task-oriented dialogues, we extended the well-known MultiWOZ dataset to encompass multi-style variations. Our findings highlight two key experimental outcomes: (i) these novel resources pose challenges for current single-style models, and (ii) multi-style resources enhance the dialogue model’s resilience to stylistic variations.