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

人们往往比他们所说的更有实际意义。举个例子，假设亚当说“我想喝杯咖啡”，鲍勃回答说：“拐角处有一个叫Joe‘s的地方。”我们将这理解为一种连贯的交流，尽管亚当的话并没有公开地作为一个问题来表达，鲍勃也没有明确地说乔的咖啡卖的是咖啡。要提取这种丰富的额外意义，我们需要考虑句子所处的语境，以及亚当和鲍勃使用语言的合作动机(称为语用推理)。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，也是未来自主智能系统发展的关键，这些系统可以使用自然语言与人类进行交流。能够在上下文中进行强大的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实用推理模型，借鉴决策理论、认知的概率模型、有限理性和语言学的想法和见解。特别是，这项工作扩展了最近发展起来的理性言语行为(RSA)模型家族，该模型提供了一套形式工具，可用于解决心理语言学中的基本挑战，即语用推理的主要原则如何脱离对话参与者之间关于合作性和共享语境的简单假设。这项事业有潜力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公开的理论空白。项目工作包括开发语义组成和语用推理的计算贝叶斯模型，并使用受控心理语言学实验测试这些模型。这项工作还将产生新的模型和公开可用的数据集，并将通过创建和加强语言学、心理学和计算机科学之间的联系来促进跨学科联系。

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.