International Research Fellowship Program: Probabilistic Models for Reasoning in Natural Language Dialog

国际研究奖学金计划：自然语言对话中推理的概率模型

• 批准号: 0853021
• 负责人: Luke Zettlemoyer
• 金额: $ 15.39万
• 依托单位: Zettlemoyer Luke
• 依托单位国家: 美国
• 项目类别: Fellowship
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-10-01 至 2011-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0853021&HistoricalAwards=false
• 关键词: International; Research; Fellowship; Program; Probabilistic

0853021ZettlemoyerThis award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The International Research Fellowship Program enables U.S. scientists and engineers to conduct nine to twenty-four months of research abroad. The program's awards provide opportunities for joint research, and the use of unique or complementary facilities, expertise and experimental conditions abroad.This award will support a twenty-four month research fellowship by Dr. Luke Zettlemoyer to work with Dr. Mark Steedman at the University of Edinburgh in the UK.The PI is developing probabilistic models for pragmatic reasoning, the type of context-dependent reasoning that is required for automated systems to participate in natural language conversations. They are investigating whether these new methods will improve performance when used in dialog systems. The focus is on two specific questions. (1) Can we develop methods for learning to recover the context-dependent meanings of a sequence of natural language statements? For each sentence, we want to be able to automatically construct a rich, logical representation of its underlying meaning. In general, later statements can elaborate on, correct, or refer to parts of previous statements, leading to a challenging context-dependent reasoning problem. (2) Can we use probabilistic, game-theoretic models of multi-agent interaction to build effective dialog systems? Such an approach will explicitly model dialog participants jointly interacting in an uncertain world where each conversant has independent beliefs and desires that influence the conversational flow. Building automated systems that participate effectively in natural language conversations is one of the classic goals of research in artificial intelligence. Such dialog systems have the potential to revolutionize the way we interact with computers. Although probabilistic techniques have been used successfully in a wide range of natural language processing problems, researchers have only recently started to develop them for modeling conversation. The methods we are developing should enable deployed systems to participate in significantly more complex conversations.

0853021Zettlemoyer 该奖项根据 2009 年美国复苏和再投资法案（公法 111-5）提供资助。国际研究奖学金计划使美国科学家和工程师能够在国外进行九到二十四个月的研究。 该项目的奖项为联合研究以及使用国外独特或互补的设施、专业知识和实验条件提供了机会。该奖项将支持 Luke Zettlemoyer 博士与 Mark Steedman 博士在大学合作的为期 24 个月的研究奖学金英国爱丁堡的 PI 正在开发实用推理的概率模型，这是自动化系统参与自然语言对话所需的上下文相关推理类型。 他们正在研究这些新方法在对话系统中使用时是否会提高性能。 重点是两个具体问题。 （1）我们能否开发出学习恢复一系列自然语言语句的上下文相关含义的方法？ 对于每个句子，我们希望能够自动构建其潜在含义的丰富逻辑表示。 一般来说，后面的陈述可以详细说明、纠正或引用之前陈述的部分内容，从而导致具有挑战性的上下文相关推理问题。 （2）我们可以使用多主体交互的概率、博弈论模型来构建有效的对话系统吗？ 这种方法将明确地模拟对话参与者在不确定的世界中共同互动，其中每个熟悉者都有影响对话流程的独立信念和愿望。 构建有效参与自然语言对话的自动化系统是人工智能研究的经典目标之一。 这种对话系统有可能彻底改变我们与计算机交互的方式。 尽管概率技术已成功应用于广泛的自然语言处理问题，但研究人员最近才开始开发它们来建模对话。 我们正在开发的方法应该使已部署的系统能够参与更加复杂的对话。