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.

人类是一种成功的物种，很大程度上是因为他们可以使用语言解释相互传递有关世界的知识。这些解释可能非常复杂，涉及对多个对象和事件类的嵌套概括。如何从自然语言中解码这些关系的准确模型可以进一步加深我们对大脑如何工作的理解，并且可以允许非程序员用户向机器解释他们想要的产品、目标和约束。句子处理实验可能为了解语言理解中的思想形成机制提供了一个重要的窗口，但人类大脑对实验控制研究设计中使用的构建刺激的陌生性异常敏感，从而产生由意想不到的单词或句子结构引起的潜在混淆效应。一种常见的替代方案是使用采用自然发生的刺激和统计控制的设计，通常在句子处理过程中使用一个或多个惊喜概率测量。不幸的是，现有的惊喜概率测量基于过于简单的句子处理模型，这些模型与语言解释可能描述的概括的嵌套结构无关，因此作为此类频率效应的预测器具有严重的局限性。因此，该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。然后，该模型将用于控制神经激活、血氧和阅读时间数据中的频率效应，以便隔离可归因于语言理解过程中构建和存储复杂想法的机械过程的效应。该项目构建了一个模型句子处理，其处理决策基于意义的心理表征，而不仅仅是单词。这意味着当重复的名词或代词指的是话语中突出的共同实体时，模型不会对这些词感到惊讶。该项目最初侧重于开发统计句子处理模型，该模型在处理每个单词后维护句子的几种可能的分析，每个分析都包含句子含义中涉及的每个话语指称的明确表示，作为与句子相邻的一组逻辑谓词。含义的图形表示中的所指对象。该模型的后续版本将这些上下文集压缩为向量，并通过循环神经网络传递。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，以确保其语言预测是准确的。然后，使用这些模型生成的增量概率来估计概率意外，作为预测现有数据集中的功能磁共振（fMRI）、脑电图（EEG）、眼动追踪和阅读时间观察的频率控制，以便隔离由于该奖项反映了 NSF 的法定使命，并且通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。