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用CCG解析和大型语言模型在大脑中建构结构的建模

Miloš Stanojević¹, Jonathan R Brennan², Donald Dunagan³

¹Google DeepMind.

Cognitive science

|July 7, 2023

概括

组合类型语法 (CCG) 在语言理解过程中更好地模拟大脑活动,而不是无上下文语法 (CFG). 这项研究表明,CCG结构建设独特地影响叶的神经信号.

科学领域:

神经科学是一个神经科学.
计算语言学计算语言学
认知科学认知科学

背景情况:

自然语言处理和机器学习工具越来越多地用于模拟语言理解.
无上下文语法 (CFG) 主要用于语法结构建模,但缺乏人类语言的表达力.
组合类型语法 (CCG) 提供了更大的表达力和直接的组成模型,用于增量解释.

研究的目的:

为了评估一个更具表现力的CCG是否在自然语言理解过程中提供了比CFG更好的人类神经信号模型.
为了比较不同的CCG变体在模拟神经数据的能力.
为了区分语法结构构建与单词可预测性的神经相关性.

主要方法:

功能磁共振成像 (fMRI) 用于收集参与者听有声书的神经信号.
该研究比较了CCG衍生指标与CFG衍生指标的合适性,以及从变压器模型中预测下一个词的基线.
测试了不同于辅助处理的CCG变体.

主要成果:

与CFG衍生的测量相比,CCG衍生的测量结果显示神经信号与CFG衍生的测量结果相比更好,特别是在左后叶.
与CCG结构构建相关的神经效应在空间上与与可预测性相关的双边高级时间效应截然不同.
这些发现表明,语法结构构建具有可分离的神经效应与自然倾听过程中的可预测性.

结论:

像CCG这样的更具表达性的语法为语言理解的神经基础提供了更好的模型,而不是表达性较低的CFG.
语法结构构建和单词可预测性涉及不同的神经机制.
这项研究强调了语法表达力在理解语言神经处理中的重要性.

关键词:

语法语法是一个词.语言建模语言建模神经网络的神经网络的神经网络分析分析分析的方法这是一个惊喜的惊喜.语法语法语法语法语法功能磁力共振成像 (fMRI) 是一种

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