A Zero-Shot Approach for Multi-User Task-Oriented Dialog Generation

Shiv Surya, Yohan Jo, Arijit Biswas, Alexandros Potamianos


In Sessions:

INLG Oral Session 5: NLG for real-world applications: (Friday, 11:30 CEST, Sun II , Watch on Zoom , Chat on Discord )

Abstract: Prior art investigating task-oriented dialog and automatic generation of such dialogs have focused on single-user dialogs between a single user and an agent. However, there is limited study on adapting such AI agents to multi-user conversations (involving multiple users and an agent). Multi-user conversations are richer than single-user conversations containing social banter and collaborative decision making. The most significant challenge impeding such studies is the lack of suitable multi-user task-oriented dialogs with annotations of user belief states and system actions. One potential solution is multi-user dialog generation from single-user data. Many single-user dialogs datasets already contain dialog state information (intents, slots), thus making them suitable candidates. In this work, we propose a novel approach for expanding single-user task-oriented dialogs (e.g. MultiWOZ) to multi-user dialogs in a zero-shot setting.