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        <title>A Survey of Challenges and Methods in the Computational Modeling of Multi-Party Dialog</title>
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    <abstract>Advances in conversational AI systems, powered in particular by large language models, have facilitated rapid progress in understanding and generating dialog. Typically, task-oriented or open-domain dialog systems have been designed to work with two-party dialog, i.e., the exchange of utterances between a single user and a dialog system. However, modern dialog systems may be deployed in scenarios such as classrooms or meetings where conversational analysis of multiple speakers is required. This survey will present research around computational modeling of “multi-party dialog”, outlining differences from two-party dialog, challenges and issues in working with multi-party dialog, and methods for representing multi-party dialog. We also provide an overview of dialog datasets created for the study of multi-party dialog, as well as tasks that are of interest in this domain.</abstract>
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    <identifier type="doi">10.18653/v1/2023.nlp4convai-1.12</identifier>
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%0 Conference Proceedings
%T A Survey of Challenges and Methods in the Computational Modeling of Multi-Party Dialog
%A Ganesh, Ananya
%A Palmer, Martha
%A Kann, Katharina
%Y Chen, Yun-Nung
%Y Rastogi, Abhinav
%S Proceedings of the 5th Workshop on NLP for Conversational AI (NLP4ConvAI 2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F ganesh-etal-2023-survey
%X Advances in conversational AI systems, powered in particular by large language models, have facilitated rapid progress in understanding and generating dialog. Typically, task-oriented or open-domain dialog systems have been designed to work with two-party dialog, i.e., the exchange of utterances between a single user and a dialog system. However, modern dialog systems may be deployed in scenarios such as classrooms or meetings where conversational analysis of multiple speakers is required. This survey will present research around computational modeling of “multi-party dialog”, outlining differences from two-party dialog, challenges and issues in working with multi-party dialog, and methods for representing multi-party dialog. We also provide an overview of dialog datasets created for the study of multi-party dialog, as well as tasks that are of interest in this domain.
%R 10.18653/v1/2023.nlp4convai-1.12
%U https://aclanthology.org/2023.nlp4convai-1.12
%U https://doi.org/10.18653/v1/2023.nlp4convai-1.12
%P 140-154

Markdown (Informal)

[A Survey of Challenges and Methods in the Computational Modeling of Multi-Party Dialog](https://aclanthology.org/2023.nlp4convai-1.12) (Ganesh et al., NLP4ConvAI 2023)

• A Survey of Challenges and Methods in the Computational Modeling of Multi-Party Dialog (Ganesh et al., NLP4ConvAI 2023)

ACL

• Ananya Ganesh, Martha Palmer, and Katharina Kann. 2023. A Survey of Challenges and Methods in the Computational Modeling of Multi-Party Dialog. In Proceedings of the 5th Workshop on NLP for Conversational AI (NLP4ConvAI 2023), pages 140–154, Toronto, Canada. Association for Computational Linguistics.