论文标题
情感二十个问题对话系统的词汇情绪智力系统
Emotion Twenty Questions Dialog System for Lexical Emotional Intelligence
论文作者
论文摘要
本文介绍了基于网络的情感演示二十个问题（EMO20Q），这是一个对话游戏，其目的是研究人们如何描述情绪。 EMO20Q也可用于开发可以玩游戏的人为智能的对话代理。在以前的工作中，EMO20Q代理使用了连续的贝叶斯机器学习模型，并且可以扮演问答角色。较新的基于变压器的神经机器学习模型使得为提问角色开发代理人成为可能。 该演示论文描述了Emo20Q游戏的提问角色的最新发展，该游戏要求代理商对更多的开放式输入做出响应。此外，我们还描述了系统的设计，包括基于Web的前端，代理体系结构和编程，以及对早期软件的更新。 演示系统将可以在ACII会议期间收集试验数据，该数据将用于为未来的实验和系统设计提供信息。
This paper presents a web-based demonstration of Emotion Twenty Questions (EMO20Q), a dialog game whose purpose is to study how people describe emotions. EMO20Q can also be used to develop artificially intelligent dialog agents that can play the game. In previous work, an EMO20Q agent used a sequential Bayesian machine learning model and could play the question-asking role. Newer transformer-based neural machine learning models have made it possible to develop an agent for the question-answering role. This demo paper describes the recent developments in the question-answering role of the EMO20Q game, which requires the agent to respond to more open-ended inputs. Furthermore, we also describe the design of the system, including the web-based front-end, agent architecture and programming, and updates to earlier software used. The demo system will be available to collect pilot data during the ACII conference and this data will be used to inform future experiments and system design.
