RI: Small: Bayesian Modeling of Situated Communicative Goals

RI：小：情景交际目标的贝叶斯建模

• 批准号: 1526723
• 负责人: Matthew Stone
• 金额: $ 49.99万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1526723&HistoricalAwards=false
• 关键词: RI; Small; Bayesian; Modeling; Situated

This multidisciplinary project undertakes a program of research in natural language generation (NLG), the subfield of artificial intelligence that aims to construct intuitive, accessible utterances to communicate the data, knowledge and reasoning of computational systems. NLG capabilities have an important role in facilitating new, more natural interaction with computers, both in current applications such as mobile information access and in emerging ones such as personal assistants and human-robot interaction. NLG systems remain inflexible and difficult to build, however. This research aims to addresses this problem by developing techniques to train NLG systems to match human language use. The project is a close collaboration that links psychological experiments, designed to uncover the strategies human speakers use, to computational experiments, which apply these strategies in NLG systems using machine learning.The theoretical framework at the center of this project is Bayesian cognitive modeling, a probabilistic approach that explains human information processing in terms of decision making under uncertainty. Applied to language use, Bayesian cognitive modeling involves estimating the communicative goals speakers adopt, the knowledge and meanings available to speakers, and the choices speakers make to express needed information in suitable linguistic terms. Such knowledge and strategies can then be used to drive NLG systems. The specific research of the project investigates three key domains for applying NLG to construct messages to describe real-world situations: making lexical choices, constructing complex linguistic structures compositionally, and fulfilling multiple overlapping communicative goals. The project explores each domain through interrelated activities carried out by an interdisciplinary team of computer scientists and psychologists: to formalize speaker choices using a range of Bayesian cognitive models; to fit the models to visually-grounded language corpora using machine learning; to evaluate the empirical scope of goal-directed reasoning by comparing the learned models both to attested human choices and to baseline learned models; and to assess how well the models match human comprehension of linguistic meaning. The intellectual merits of the project lie in bridging the gap between traditional goal-directed rational models of human behavior and state-of-the-art computational methods that instantiate templates or reproduce likely patterns. In addition to the societal impacts of the technology, the broader impacts of the project include the construction of data resources, models and modeling tools that will be distributed to facilitate further research, and contributions to ongoing initiatives for education in cognitive science at Rutgers.

这个多学科项目承担了自然语言生成（NLG）的研究计划，NLG是人工智能的子领域，旨在构建直观，可访问的话语来传达计算系统的数据，知识和推理。 无论是在移动的信息访问等当前应用中，还是在个人助理和人机交互等新兴应用中，NLG功能都在促进与计算机的新的、更自然的交互方面发挥着重要作用。 然而，NLG系统仍然不灵活，难以构建。 这项研究旨在通过开发技术来训练NLG系统以匹配人类语言使用来解决这个问题。 该项目是一项紧密合作，将旨在揭示人类说话者使用的策略的心理学实验与将这些策略应用于使用机器学习的NLG系统的计算实验相结合。该项目的核心理论框架是贝叶斯认知建模，这是一种概率方法，可以从不确定性决策的角度解释人类信息处理。 应用于语言使用，贝叶斯认知模型涉及估计说话人采用的交际目标，说话人可用的知识和意义，以及说话人用合适的语言术语表达所需信息的选择。 这些知识和策略可以用于驱动NLG系统。 该项目的具体研究探讨了应用NLG构建信息来描述真实世界情景的三个关键领域：词汇选择，组合构建复杂的语言结构，以及实现多个重叠的交际目标。 该项目通过由计算机科学家和心理学家组成的跨学科小组开展的相互关联的活动探索每个领域：使用一系列贝叶斯认知模型正式确定说话人的选择;使用机器学习将模型与基于视觉的语言语料库相匹配;通过将学习模型与经证实的人类选择和基线学习模型进行比较，评估目标导向推理的经验范围;并评估这些模型与人类对语言意义的理解的匹配程度。 该项目的智力优势在于弥合了传统的目标导向的人类行为理性模型与最先进的计算方法之间的差距，这些计算方法可以实例化模板或复制可能的模式。 除了该技术的社会影响外，该项目更广泛的影响还包括构建数据资源，模型和建模工具，这些工具将被分发以促进进一步的研究，并为罗格斯大学正在进行的认知科学教育计划做出贡献。