Collaborative Research: CompCog: Broad-coverage probabilistic models of communication in context

协作研究：CompCog：上下文中通信的广泛覆盖概率模型

• 批准号: 1456081
• 负责人: Roger Levy
• 金额: $ 27.9万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-15 至 2018-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1456081&HistoricalAwards=false
• 关键词: Collaborative; Research; CompCog; Broad; coverage

People often mean more than they say. To take an example, imagine Adam says "I could use a cup of coffee" and Bob responds by saying "There's a place called Joe's around the corner." We understand this as a coherent exchange even though Adam's utterance wasn't phrased overtly as a question and Bob didn't explicitly say that Joe's sells coffee. Extracting this rich additional meaning requires us to consider sentences in light of both the context they are used in and the cooperative motivations of Adam and Bob in using language (what are called "pragmatic inferences"). This project is devoted to constructing formal models of these pragmatic inferences. Modeling pragmatic inference is a major scientific challenge in the study of language and the human mind and a key to the future development of autonomous intelligent systems that can communicate with humans using natural language. Machines that can do robust language understanding in context will pave the way for societally beneficial technological applications such as adaptive intelligent tutoring and assistive technologies. The technical core of the project involves developing and extending models of pragmatic reasoning, drawing on ideas and insights from decision theory, probabilistic models of cognition, bounded rationality, and linguistics. In particular, the work extends the recently developed family of "rational speech act" (RSA) models, which provides a set of formal tools that can be used to address basic challenges in psycholinguistics concerning how major principles of pragmatic inference fall out of simple assumptions about cooperativity and shared context among conversation participants. This enterprise has the potential to fill a major open theoretical gap in our scientific understanding of human language and social cognition. Project work includes developing computational Bayesian models of semantic composition and pragmatic inference and testing those models using controlled psycholinguistic experiments. The work will also yield new models and publicly available datasets and will contribute to interdisciplinary connections by creating and reinforcing links between linguistics, psychology, and computer science.

人们通常的意思比他们说的要多。举个例子，想象亚当说：“我可以用一杯咖啡”，鲍勃回答说：“有一个叫乔的地方在拐角处。”尽管亚当的话语并没有公开地说，但鲍勃并没有明确说乔的咖啡，我们也理解这是一种连贯的交流。提取这种丰富的额外含义需要我们根据使用语言（所谓的“务实推断”）来考虑它们所在的上下文以及亚当和鲍勃的合作动机。该项目致力于构建这些务实推论的形式模型。建模务实的推论是语言和人类思想研究的主要科学挑战，也是可以使用自然语言与人类交流的自主智能系统未来发展的关键。可以在上下文中进行强大语言理解的机器将为社会有益的技术应用铺平道路，例如自适应智能辅导和辅助技术。该项目的技术核心涉及开发和扩展务实推理的模型，借鉴决策理论的思想和见解，认知概率模型，有限理性和语言学。 特别是，这项工作扩展了最近开发的“理性言语法案”（RSA）模型，该模型提供了一组正式工具，可用于解决心理语言学中的基本挑战，这些挑战涉及务实推论的主要原则如何脱离对话参与者之间关于合作和共享背景的简单假设。该企业有可能在我们对人类语言和社会认知的科学理解中填补一个重大的公开理论差距。项目工作包括开发语义组成和务实推理的计算贝叶斯模型，并使用受控的心理语言实验测试这些模型。 这项工作还将产生新的模型和公开可用的数据集，并通过建立和加强语言学，心理学和计算机科学之间的联系来促进跨学科的联系。