CompCog: Noisy-channel processing in human language understanding

CompCog：人类语言理解中的噪声通道处理

• 批准号: 2121074
• 负责人: Roger Levy
• 金额: $ 59.98万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2121074&HistoricalAwards=false
• 关键词: CompCog; Noisy; channel; processing; human

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.

每天，我们都会了解几百个从未遇到过的句子，我们会产生数百个句子。鉴于语言发生的噪音环境，扬声器的错误以及我们的记忆和注意力的局限性，这一成功是显着的。本项目开发并测试了一种强大的语言理解理论。研究人员将信息理论的工具，自然语言处理，语言学和实验心理学结合在一起，以提供数学形式化的人类语言理解模型，作为对“嘈杂渠道”的概率推断。该项目有助于我们对人类语言和人类思想的基本科学理解，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，并识别和指导对发展和获得性语言疾病的治疗。在这个研究计划中，研究人员在计算和算法上开发了一种关于人类对句子的理解如何逐步展现的精确理论。这种增量的噪声通道理论是使用最先进的符号和基于神经网络的方法来实施的，从人工智能和自然语言处理中建模语言。一个关键组件包括一个说明语言形状的分布统计信息在实时语言处理过程中使用的嘈杂记忆表示。关于鲁棒性对语言输入中的鲁棒性的独特经验预测以及所提出的机制何时以及如何影响理解，允许将这种方法相对于替代性心理语言理论进行评估。使用受控的行为实验测试了这些预测，以了解母语者的过程和解释语言输入。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估标准，被认为是值得通过评估的。

项目成果

Flexible Generation from Fragmentary Linguistic Input

• DOI: 10.18653/v1/2022.acl-long.563
• 发表时间: 2022
• 作者: Peng Qian;R. Levy

It is not what you say but how you say it: Evidence from Russian shows robust effects of the structural prior on noisy channel inferences.

重要的不是你说什么，而是你怎么说：来自俄语的证据表明，结构先验对噪声通道推论具有强大的影响。

• DOI: 10.1037/xlm0001244
• 发表时间: 2023
• 期刊: and Cognition
• 作者: Poliak, Moshe;Ryskin, Rachel;Braginsky, Mika;Gibson, Edward
• 通讯作者: Gibson, Edward

The effect of context on noisy-channel sentence comprehension

上下文对噪声通道句子理解的影响

• DOI: 10.1016/j.cognition.2023.105503
• 发表时间: 2023
• 期刊: Cognition
• 影响因子: 3.4
• 作者: Chen, Sihan;Nathaniel, Sarah;Ryskin, Rachel;Gibson, Edward
• 通讯作者: Gibson, Edward

A Cross-Linguistic Pressure for Uniform Information Density in Word Order

词序统一信息密度的跨语言压力

• DOI: 10.1162/tacl_a_00589
• 发表时间: 2023
• 期刊: Transactions of the Association for Computational Linguistics
• 影响因子: 10.9
• 作者: Clark, Thomas Hikaru;Meister, Clara;Pimentel, Tiago;Hahn, Michael;Cotterell, Ryan;Futrell, Richard;Levy, Roger
• 通讯作者: Levy, Roger

Using Computational Models to Test Syntactic Learnability

使用计算模型来测试句法可学习性

• DOI: 10.1162/ling_a_00491
• 发表时间: 2023
• 期刊: Linguistic Inquiry
• 影响因子: 1.6
• 作者: Wilcox, Ethan Gotlieb;Futrell, Richard;Levy, Roger
• 通讯作者: Levy, Roger