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

Ananya Ganesh, Martha Palmer, Katharina Kann
{"title":"A Survey of Challenges and Methods in the Computational Modeling of Multi-Party Dialog","authors":"Ananya Ganesh, Martha Palmer, Katharina Kann","doi":"10.18653/v1/2023.nlp4convai-1.12","DOIUrl":null,"url":null,"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.","PeriodicalId":169166,"journal":{"name":"Proceedings of the 5th Workshop on NLP for Conversational AI (NLP4ConvAI 2023)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 5th Workshop on NLP for Conversational AI (NLP4ConvAI 2023)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18653/v1/2023.nlp4convai-1.12","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

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.
多方对话计算建模的挑战与方法综述
对话式人工智能系统的进步,特别是在大型语言模型的推动下,促进了理解和生成对话方面的快速进展。通常,面向任务的或开放域的对话系统被设计为使用双方对话,即单个用户和对话系统之间的话语交换。然而,现代对话系统可能部署在需要对多个说话者进行会话分析的教室或会议等场景中。本调查将围绕“多方对话”的计算建模进行研究,概述与两方对话的区别,处理多方对话的挑战和问题,以及表示多方对话的方法。我们还概述了为研究多方对话而创建的对话数据集,以及在该领域感兴趣的任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信