RI: Small: Bayesian Modeling of Situated Communicative Goals

这个多学科项目承担了一个自然语言生成（NLG）的研究项目，自然语言生成是人工智能的子领域，旨在构建直观、可访问的话语，以交流计算系统的数据、知识和推理。NLG能力在促进新的、更自然的与计算机的交互方面发挥着重要作用，无论是在当前的应用中，如移动信息访问，还是在新兴的应用中，如个人助理和人机交互。然而，NLG系统仍然不灵活，难以构建。本研究旨在通过开发训练NLG系统以匹配人类语言使用的技术来解决这个问题。该项目是一个密切合作的项目，将心理学实验与计算实验联系起来，这些实验旨在揭示人类说话者使用的策略，计算实验将这些策略应用于使用机器学习的NLG系统。这个项目的核心理论框架是贝叶斯认知模型，这是一种概率方法，解释了人类在不确定情况下的信息处理决策。贝叶斯认知模型应用于语言使用，包括估计说话者采用的交际目标，说话者可获得的知识和意义，以及说话者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来驱动NLG系统。本项目的具体研究探讨了运用NLG构建描述现实情景的信息的三个关键领域：词汇选择、组合构建复杂的语言结构和实现多个重叠的交际目标。该项目通过一个由计算机科学家和心理学家组成的跨学科团队开展的相互关联的活动来探索每个领域：使用一系列贝叶斯认知模型形式化说话人的选择；使用机器学习将模型拟合到基于视觉的语言语料库；通过将学习模型与经过验证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是否匹配。该项目在智力上的优点在于弥合了人类行为的传统目标导向的理性模型和实例化模板或复制可能模式的最先进的计算方法之间的差距。除了该技术的社会影响外，该项目的更广泛影响还包括数据资源、模型和建模工具的构建，这些将被分发以促进进一步的研究，并为罗格斯大学认知科学教育的持续倡议做出贡献。

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.