CompCog: Noisy-channel processing in human language understanding

每天，我们都能理解数百个我们从未遇到过的句子，并创造出更多的句子。考虑到语言发生的嘈杂环境、说话者所犯的错误以及我们的记忆和注意力的局限性，这种成功是了不起的。本项目开发并测试了一种强大的语言理解理论。研究人员结合了信息论、自然语言处理、语言学和实验心理学的工具，提供了一个数学形式化的人类语言理解模型，作为“噪声信道”上的概率推断。该项目有助于我们对人类语言和人类思维的基本科学理解，同时加强心理语言学和当代人工智能研究之间的桥梁。这项工作具有广泛的长期潜力，可以增强我们对语言领域健康认知表现和发展的理解，并确定和指导发育和获得性语言障碍的治疗。在这个研究项目中，研究人员开发了一种计算和算法精确的理论，研究人类对句子的理解是如何每时每刻展开的。这种增量噪声信道理论是使用最先进的符号和基于神经网络的方法来实现的，这些方法来自人工智能和自然语言处理。一个关键组成部分包括在实时语言处理过程中如何使用语言形状噪声记忆表示的分布统计。关于语言输入中错误的稳健性以及所提出的机制何时以及如何影响理解的独特经验预测，使这种方法能够相对于其他心理语言学理论进行评估。这些预测是通过控制行为实验来测试母语人士如何处理和解释语言输入的。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Every day we understand hundreds of sentences that we have never encountered and we produce hundreds more. This success is remarkable given the noisy environments in which language takes place, the errors speakers make, and limitations of our memory and attention. The present project develops and tests a theory of robust language understanding. The investigators combine tools of information theory, natural language processing, linguistics, and experimental psychology to provide a mathematically formalized model of human language comprehension as probabilistic inference over a “noisy channel”. The project contributes to our basic scientific understanding of human language and the human mind, while strengthening bridges between psycholinguistics and contemporary artificial intelligence research. The work has wide-ranging long-term potential to enhance our understanding of healthy cognitive performance and development in the area of language and to identify and guide treatments for developmental and acquired language disorders. In this program of research, the investigators develop a computationally and algorithmically precise theory of how human understanding of sentences unfolds moment-by-moment. This incremental noisy-channel theory is implemented using state-of-the-art symbolic and neural network-based approaches to modeling language from artificial intelligence and natural language processing. A key component includes an account of how the distributional statistics of language shape noisy memory representations used during real-time language processing. Distinctive empirical predictions regarding robustness to errors in the linguistic input and regarding when and how the proposed mechanisms influence comprehension, allow this approach to be evaluated relative to alternative psycholinguistic theories. The predictions are tested using controlled behavioral experiments on how native speakers process and interpret linguistic input.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.