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.- Anthology ID:
- 2023.inlg-main.14
- Volume:
- Proceedings of the 16th International Natural Language Generation Conference
- Month:
- September
- Year:
- 2023
- Address:
- Prague, Czechia
- Editors:
- C. Maria Keet, Hung-Yi Lee, Sina Zarrieß
- Venues:
- INLG | SIGDIAL
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 196–205
- Language:
- URL:
- https://aclanthology.org/2023.inlg-main.14
- DOI:
- 10.18653/v1/2023.inlg-main.14
- Cite (ACL):
- Shiv Surya, Yohan Jo, Arijit Biswas, and Alexandros Potamianos. 2023. A Zero-Shot Approach for Multi-User Task-Oriented Dialog Generation. In Proceedings of the 16th International Natural Language Generation Conference, pages 196–205, Prague, Czechia. Association for Computational Linguistics.
- Cite (Informal):
- A Zero-Shot Approach for Multi-User Task-Oriented Dialog Generation (Surya et al., INLG-SIGDIAL 2023)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/2023.inlg-main.14.pdf