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: 1456077
• 负责人: Michael Frank
• 金额: $ 42.79万
• 依托单位: Stanford University
• 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 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1456077&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" 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.

人们往往言过其实。举个例子，假设亚当说“我想喝杯咖啡”，鲍勃回答说“街角有一家叫Joe’s的地方”。我们认为这是一个连贯的交流，尽管亚当的话并没有明显地表达成一个问题，鲍勃也没有明确地说乔卖咖啡。提取这种丰富的附加意义需要我们考虑句子的上下文和Adam和Bob使用语言的合作动机（即所谓的“语用推理”）。本项目致力于构建这些语用推理的形式模型。语用推理建模是语言和人类思维研究中的一项重大科学挑战，也是未来发展能够使用自然语言与人类交流的自主智能系统的关键。能够在上下文中进行强大语言理解的机器将为自适应智能辅导和辅助技术等对社会有益的技术应用铺平道路。该项目的技术核心涉及开发和扩展语用推理模型，借鉴决策理论、认知概率模型、有限理性和语言学的思想和见解。特别是，这项工作扩展了最近开发的“理性言语行为”模型家族，该模型提供了一套正式工具，可用于解决心理语言学中的基本挑战，即语用推断的主要原则如何脱离对话参与者之间的合作和共享上下文的简单假设。这项事业有可能填补我们对人类语言和社会认知的科学理解中的一个重大理论缺口。项目工作包括开发语义组成和语用推断的计算贝叶斯模型，并使用受控心理语言学实验对这些模型进行测试。这项工作还将产生新的模型和公开可用的数据集，并将通过建立和加强语言学、心理学和计算机科学之间的联系，为跨学科的联系做出贡献。