Unsupervised Extraction of Dialogue Policies from Conversations

arXiv:2406.15214v1 Announce Type: new
Abstract: Dialogue policies play a crucial role in developing task-oriented dialogue systems, yet their development and maintenance are challenging and typically require substantial effort from experts in dialogue modeling. While in many situations, large amounts of conversational data are available for the task at hand, people lack an effective solution able to extract dialogue policies from this data. In this paper, we address this gap by first illustrating how Large Language Models (LLMs) can be instrumental …

abstract arxiv conversational conversational data conversations cs.cl data development dialogue experts extraction maintenance modeling people policies role systems type unsupervised while