EAGER: Collaborative Research: Modeling Distinctive Partners in Adaptive Spoken Dialog

EAGER：协作研究：在自适应口语对话中建模独特的合作伙伴

• 批准号: 1043665
• 负责人: Susan Brennan
• 金额: $ 14万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2013-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1043665&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Modeling; Distinctive

The human ability to use language flexibly is a hallmark of robust intelligence. In interactive dialog, utterances are dynamically tailored to the common ground or specific context with specific partners. However, interaction with spoken dialog systems is highly constrained and constraining, allowing speakers very little flexibility in what they can say while the system presents pre-determined messages. To make interactive dialog technology broadly useful, this exploratory interdisciplinary project collects a corpus of dialogs exhibiting some important sources of variation, analyzes the corpus, and uses the resulting analyses to develop models and prototype implementations of dynamic dialog strategies. The ultimate goal of this effort is to support the synthesis of entirely new, flexible, and robust spoken dialog systems that are capable of adapting on-line. The Walking-Around corpus consists of 40 human-human dialog interactions where a remotely located person gives directions to a pedestrian walking around in an urban or campus environment. The experimental paradigm varies the friendship relationship of the dialog partners, whether the director can see what the pedestrian sees, and the familiarity of both the director and the pedestrian with the environment. No other existing direction-giving corpora model dialog interaction in an outdoor real-time environment where the physical context grounds the dialog context. The resulting corpus is used to test hypotheses about, and develop models of, the evolution of local and global dialog adaptation strategies. Key to our effort is determining which adaptations are actually functional, that is, beneficial for a particular task or context in spoken dialog systems.

人类灵活使用语言的能力是强大智力的标志。在交互式对话中，话语被动态地定制为与特定伙伴的共同基础或特定上下文。然而，与口语对话系统的交互是高度受限和约束的，在系统呈现预定消息的同时，允许说话者在他们可以说什么方面非常小的灵活性。为了使交互式对话技术广泛使用，这个探索性的跨学科项目收集了一个语料库的对话表现出一些重要的变化来源，分析语料库，并使用由此产生的分析，开发模型和原型实现的动态对话策略。这项工作的最终目标是支持合成全新的，灵活的，强大的口语对话系统，能够适应在线。Walking-Around语料库由40个人与人之间的对话交互组成，其中远程定位的人向在城市或校园环境中走动的行人提供方向。实验范式改变了对话伙伴的友谊关系，导演是否能看到行人所看到的，以及导演和行人对环境的熟悉程度。没有其他现有的方向给出语料库模型在户外实时环境中的对话交互的物理上下文的基础上的对话上下文。由此产生的语料库被用来测试假设，并开发模型，本地和全球对话适应策略的演变。我们努力的关键是确定哪些适应实际上是功能性的，也就是说，对口语对话系统中的特定任务或上下文有益。