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        <title>A Zero-Shot Approach for Multi-User Task-Oriented Dialog Generation</title>
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        <namePart type="given">Shiv</namePart>
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    <name type="personal">
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2023.inlg-main.14</identifier>
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%0 Conference Proceedings
%T A Zero-Shot Approach for Multi-User Task-Oriented Dialog Generation
%A Surya, Shiv
%A Jo, Yohan
%A Biswas, Arijit
%A Potamianos, Alexandros
%Y Keet, C. Maria
%Y Lee, Hung-Yi
%Y Zarrieß, Sina
%S Proceedings of the 16th International Natural Language Generation Conference
%D 2023
%8 September
%I Association for Computational Linguistics
%C Prague, Czechia
%F surya-etal-2023-zero
%X 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.
%R 10.18653/v1/2023.inlg-main.14
%U https://aclanthology.org/2023.inlg-main.14
%U https://doi.org/10.18653/v1/2023.inlg-main.14
%P 196-205

Markdown (Informal)

[A Zero-Shot Approach for Multi-User Task-Oriented Dialog Generation](https://aclanthology.org/2023.inlg-main.14) (Surya et al., INLG-SIGDIAL 2023)

• A Zero-Shot Approach for Multi-User Task-Oriented Dialog Generation (Surya et al., INLG-SIGDIAL 2023)

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.