基于变压器的模型捕捉了在口语叙述中名词和动词之间的神经表示差异
概括
人类和深度神经网络 (DNN) 与名词和动词一样代表词类. 这项研究使用脑电图 (EEG) 和BERT模型来显示可比的神经和人工智能语言表示.
科学领域:
- 认知神经科学 认知神经科学
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 名词和动词是语言的核心组成部分,在人类大脑和深度神经网络 (DNN) 中具有不同的表示.
- 之前的研究强调了生物大脑和人工系统如何处理单词类的差异.
研究的目的:
- 为了调查人类和DNN是否在通道上下文中代表了词类差异.
- 将人类对名词和动词的神经反应与DNN中的表示进行比较.
主要方法:
- 利用脑电图 (EEG) 记录人类神经对名词和动词在叙事听力过程中的反应.
- 分析了基于变压器的DNNBERT的词嵌入,用于语言表示.
- 与人类EEG反应相关联的DNN表示.
主要成果:
- 在人类中观察到名词和动词之间EEG反应的显著差异.
- 在BERT模型表示中确定了可比的词类差异.
- 证明DNN表示可以预测人类EEG响应差异.
结论:
- DNN可以开发类似人类的语言表示.
- DNN的隐藏层嵌入捕获了在人类大脑活动中发现的词类差异.
- 表明生物和人工系统如何处理基本语言结构的趋同.
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