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RI: Small:Comp Cog: Broad-coverage semantic models of human sentence processing

RI：Small：Comp Cog：人类句子处理的广泛覆盖语义模型

基本信息

批准号：
1816891
负责人：
William Schuler
金额：
$ 49.03万
依托单位：
Ohio State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-15 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1816891&HistoricalAwards=false
关键词：
RI Small Comp Cog Broad

项目摘要

Humans are a successful species in large part because they can pass knowledge about the world to one another using linguistic explanations. These explanations can be quite complex, involving nested generalizations about multiple classes of objects and events. Accurate models of how these relationships are decoded from natural language could further our understanding of how the brain works, and may allow non-programmer users to explain their desired products, goals and constraints to machines. Sentence processing experiments may provide an important window into the mechanisms of idea formation in language comprehension, but the human mind is extraordinarily sensitive to the strangeness of constructed stimuli used in experimentally controlled research designs, yielding potentially confounding effects arising from unexpected words or sentence structures. A common alternative is to use designs employing naturally-occurring stimuli with statistical controls, usually using one or more probabilistic measures of surprise during sentence processing. Unfortunately, existing probabilistic measures of surprise are based on overly simple models of sentence processing that are not connected to the nested structure of generalizations that a linguistic explanation may describe, and thus have severe limits as predictors of these kinds of frequency effects. This project will therefore develop a sentence processing model that decodes sentences into meanings using a human-like incremental probabilistic process. This model will then be used to control for frequency effects in neural activation, blood oxygenation and reading time data in order to isolate effects that can be attributed to the mechanical process of constructing and storing complex ideas during language comprehension.This project constructs a model of sentence processing that bases its processing decisions on mental representations of meanings rather than on words only. This means that the model will be less surprised by repeated nouns or pronouns when these words refer to a common entity which is prominent in a discourse. The project initially focuses on the development of a statistical sentence processing model which maintains several possible analyses of a sentence after each word is processed, each of which contains explicit representations of each discourse referent involved in a sentence meaning as a set of logical predicates adjacent to that referent in a graphical representation of the meaning. A subsequent version of the model compresses these context sets into vectors, which are passed through a recurrent neural network. The predictions of these models are compared against existing neural network language models used in natural language processing applications to ensure that their linguistic predictions are accurate. Incremental probabilities generated by these models are then used to estimate probabilistic surprise as a frequency control in predicting functional magnetic resonance (fMRI), electroencephalographic (EEG), eye-tracking, and reading-time observations in existing datasets, in order to isolate effects due to memory usage and other mechanistic factors.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.

人类之所以是一个成功的物种，很大程度上是因为他们可以通过语言解释来相互传递关于世界的知识。这些解释可能相当复杂，涉及到对多类对象和事件的嵌套概括。如何从自然语言中解码这些关系的精确模型可以进一步了解大脑如何工作，并允许非程序员用户向机器解释他们想要的产品，目标和限制。句子加工实验可能为语言理解中的想法形成机制提供了一个重要的窗口，但人类的大脑对实验控制研究设计中使用的构造刺激的陌生性非常敏感，可能会产生意想不到的单词或句子结构所产生的混淆效应。一种常见的替代方法是使用自然发生的刺激与统计控制的设计，通常使用一个或多个概率测量的惊喜在句子处理。不幸的是，现有的概率惊讶的措施是基于过于简单的句子处理模型，没有连接到嵌套结构的概括，语言学解释可能会描述，因此有严重的限制，这些类型的频率效应的预测。因此，本项目将开发一个句子处理模型，使用类似人类的增量概率过程将句子解码为含义。这个模型将被用来控制神经激活，血氧和阅读时间数据的频率效应，以隔离的影响，可以归因于机械过程中构建和存储复杂的想法在语言理解。这个项目构建了一个模型的句子处理的基础上的心理表征的意义，而不是只对单词的处理决定。这意味着，当这些词指的是在话语中突出的共同实体时，模型对重复的名词或代词不会感到惊讶。该项目最初侧重于开发一个统计句子处理模型，该模型在处理每个单词后对句子进行几种可能的分析，每个分析都包含句子含义中涉及的每个话语所指对象的明确表示，作为一组逻辑谓词，与所指对象相邻，以图形表示含义。模型的后续版本将这些上下文集压缩成向量，这些向量通过递归神经网络传递。将这些模型的预测与自然语言处理应用中使用的现有神经网络语言模型进行比较，以确保其语言预测准确。然后，这些模型生成的增量概率用于估计概率惊喜，作为预测功能性磁共振（fMRI）、脑电图（EEG）、眼动跟踪和现有数据集中阅读时间观察的频率控制。该奖项反映了NSF的法定使命，并通过使用基金会的学术价值和更广泛的影响审查标准。