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.

本文介绍了 Emotion Twenty Questions (EMO20Q) 的基于网络的演示，它是一个对话游戏，旨在研究人们如何描述情感。EMO20Q 还可用于开发能够玩游戏的人工智能对话代理程序。该系统的最新发展，包括基于变压器的神经机器学习模型和用于回答问题的系统架构和编程等方面的更新。该演示系统将可用于收集试点数据。