神经网络作为处理语法约束的认知模型
1Department of Mathematics, Statistics, and Computer Science, Macalester College, Saint Paul, MN, USA.
Open mind : discoveries in cognitive science
|May 15, 2024
概括
神经网络可以模拟人类主体-动词协议错误,但不完美. 训练数据显著影响模型行为,突出了完全捕捉人类语法处理的局限性.
科学领域:
- 计算语言学计算语言学
- 心理语言学 心理语言学
- 认知科学是一种认知科学.
背景情况:
- 语言依赖于语法约束,如主体-动词协议,语法性.
- 演讲者经常犯协议错误,特别是由于协议吸引与中间的名词短语.
研究的目的:
- 研究神经网络语言模型,特别是长期短期记忆 (LSTM) 网络,作为理解人类协议错误的框架.
- 将LSTM网络的协议错误模式与人类实验文献中观察到的协议错误模式进行比较.
主要方法:
- 分析LSTM网络产生的协议错误,训练在大型文本体上.
- 将模型生成的错误与人类错误模式进行比较,考虑数字不对称性和句子语法性等因素.
- 评估显式语法监督和语料库特征对网络行为的影响.
主要成果:
- LSTM网络成功模拟了数字不对称性和语法和非语法句子之间的吸引力差异等现象.
- 模型无法复制人类对语法距离和概念数的敏感性.
- 明确的语法监督并不总是产生更类似人类的语法行为.
- 培训集体对网络产生的协议错误模式产生了重大影响.
结论:
- 神经网络为模拟人类语法处理的某些方面提供了有价值的框架,包括协议错误.
- 目前的模型在捕捉人类语法行为的全部复杂性方面存在局限性,例如对语法距离和概念数的敏感性.
- 训练数据的选择是塑造神经网络语言行为的一个关键因素.
- 需要进一步的研究来完善神经网络架构和培训方法,以便更全面地建模人类语言的获取和处理.
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