2023 ACL ACL 2023

A Survey of Challenges and Methods in the Computational Modeling of Multi-Party Dialog

Abstract

AbstractAdvances 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.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Natural Language Processing
🧭 Keyword Pioneer — multi-party dialog
🐝 Cross-Pollinator — Artificial Intelligence, Computer Vision, Deep Learning, Interdisciplinary, Machine Learning, Natural Language Processing, Speech & Audio