CAREER: Prevention, Detection and Repair of Miscommunication in Spoken Natural Language Dialog Processing Systems

最近语音识别技术的进步使口语自然语言对话成为一种可行的人机交互方式。一个重要的悬而未决的问题是对错误沟通的处理。通过研究以前记录的人-人和人-机对话，将研究以下减少自然语言对话中的错误交流的策略：(1)扩展期望驱动的输入理解模型；(2)开发用于选择性验证用户输入的基于上下文的模型；以及(3)开发参与子对话以解决错误交流的方法。这些理论将在模拟和实验条件下实施和评估。该项目的结果将为评估当前对话理论的充分性提供一个新的基准，并为口语人机对话的特征提供进一步的数据。该项目还将开发几个丰富的教育项目，以支持北卡罗来纳州以农村为主、经济欠发达的东部地区。

Recent improvements in speech recognition technology has made spoken natural language dialog a viable means of human-computer interaction. An important unresolved issue is the handling of miscommunication. By studying previously recorded human-human and human-computer dialogs, the following strategies for reducing miscommunication in natural language dialog will be investigated: ( 1) extending an expectation-driven model for input understanding; (2) developing a context-based model for selective verification of user inputs; and (3) developing methods for engaging in subdialogs for resolving miscommunications. These theories will be implemented and evaluated under both simulated and experimental conditions. The results from this project will provide a new benchmark in evaluating the adequacy of current dialog theory as well as provide further data on the characteristics of spoken human-machine dialog. The project will also develop several enrichment educational programs in support of the predominantly rural and less economically developed eastern part of North Carolina.