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

0853021 Zettlemoyer该奖项由2009年美国复苏和再投资法案(公法111-5)资助。国际研究奖学金计划允许美国科学家和工程师在国外进行9至24个月的研究。该项目的奖项提供了联合研究的机会，并利用国外独特或互补的设施、专业知识和实验条件。该奖项将支持Luke Zettlemoyer博士与英国爱丁堡大学的Mark Steedman博士合作的为期24个月的研究奖学金。PI正在开发实用推理的概率模型，这是自动系统参与自然语言对话所需的一种上下文相关推理类型。他们正在调查这些新方法在对话系统中使用时是否会提高性能。焦点集中在两个具体问题上。(1)我们能开发出学习恢复一系列自然语言语句的上下文相关意义的方法吗？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。通常，后面的语句可以详细说明、更正或引用前面语句的一部分，从而导致具有挑战性的上下文相关推理问题。(2)我们能否使用多智能体交互的概率博弈论模型来建立有效的对话系统？这种方法将明确地对对话参与者在不确定世界中联合交互进行建模，在该世界中，每个熟人都具有影响对话流的独立信念和愿望。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彻底改变我们与计算机交互的方式。虽然概率技术已经成功地应用于一系列自然语言处理问题，但研究人员最近才开始将其用于会话建模。我们正在开发的方法应该使部署的系统能够参与更复杂的对话。

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.