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.

0853021zettlemoyerthis奖是根据2009年的《美国复苏与再投资法》（公法111-5）资助的。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至二十四个月的研究。 该计划的奖项提供了共同研究的机会，以及在国外使用独特或补充设施，专业知识和实验条件。该奖项将支持二十四个月的研究奖学金，卢克·泽特勒莫耶（Luke Zettlemoyer）博士与英国爱丁堡大学的马克·史蒂德曼（Mark Steedmoyer）博士一起与概念性的自然依赖性依赖于特定的自然情节，以实现概率的范围，以实现概率的方式。对话。 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）我们可以开发学习恢复一系列自然语言陈述的上下文含义的方法吗？ 对于每个句子，我们希望能够自动构建其基本含义的丰富，逻辑表示。 通常，以后的陈述可以详细说明，纠正或参考先前陈述的一部分，从而导致具有挑战性的上下文依赖性推理问题。 （2）我们可以使用概率，游戏理论模型的多代理交互作用来构建有效的对话系统吗？ 这种方法将明确模拟对话框参与者在不确定的世界中共同互动的参与者，在这个世界中，每个熟悉的人都有影响对话流程的独立信念和欲望。 建立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。 这样的对话系统有可能改变我们与计算机互动的方式。 尽管概率技术已成功地用于广泛的自然语言处理问题，但研究人员直到最近才开始开发它们以建模对话。 我们正在开发的方法应该使部署的系统能够参与更加复杂的对话。