论文标题
学习基本数学的口语对话系统的端到端评估
End-to-End Evaluation of a Spoken Dialogue System for Learning Basic Mathematics
论文作者
论文摘要
用于构建教育应用的基于语言的人工智能（AI）技术的进步可以为社交机会提供AI，并具有更大的积极影响。在许多学科中，提高数学教育的质量对于在年轻时建立批判性思维和解决问题的技能至关重要。会话AI系统已经开始成熟到可以在帮助学生学习基本数学概念方面发挥重要作用的地步。这项工作介绍了一个面向任务的口语对话系统（SD），旨在支持基于游戏的基本数学概念学习幼儿教育。该系统已通过学校的现实部署进行评估，同时学生正在通过多模式互动练习早期数学概念。我们讨论了改善用于数学学习的SDS管道的努力，我们利用Mathbert表示探索了对自然语言理解（NLU）模块的潜在增强。我们使用自动语音识别（ASR），意图识别和对话经理（DM）组件的现实部署输出进行端到端评估，以了解错误传播如何影响现实世界情景中的整体性能。
The advances in language-based Artificial Intelligence (AI) technologies applied to build educational applications can present AI for social-good opportunities with a broader positive impact. Across many disciplines, enhancing the quality of mathematics education is crucial in building critical thinking and problem-solving skills at younger ages. Conversational AI systems have started maturing to a point where they could play a significant role in helping students learn fundamental math concepts. This work presents a task-oriented Spoken Dialogue System (SDS) built to support play-based learning of basic math concepts for early childhood education. The system has been evaluated via real-world deployments at school while the students are practicing early math concepts with multimodal interactions. We discuss our efforts to improve the SDS pipeline built for math learning, for which we explore utilizing MathBERT representations for potential enhancement to the Natural Language Understanding (NLU) module. We perform an end-to-end evaluation using real-world deployment outputs from the Automatic Speech Recognition (ASR), Intent Recognition, and Dialogue Manager (DM) components to understand how error propagation affects the overall performance in real-world scenarios.
