更深层次的问题是语言的神经网络模型.
Thomas G Bever1, Noam Chomsky1, Sandiway Fong1
1Department of Linguistics, University of Arizona, Tucson, AZ, USA tgb@arizona.edu nchomsky3@gmail.com Sandiway@arizona.edu Massimo@arizona.eduhttps://bever.infohttps://chomsky.infohttps://sandiway.arizona.eduhttps://massimo.sbs.arizona.edu.
深度神经网络 (DNN) 模型是工程壮举,但作为语言的科学模型失败了. 他们忽视了关键的生物学,发育和自然原则,这些原则对于思想和大脑中语言组织至关重要.
科学领域:
- 认知科学 认知科学
- 神经科学是一个神经科学.
- 计算语言学 计算语言学
背景情况:
- 深度神经网络 (DNN) 代表了在建模语言行为方面取得的重大工程成就.
- 然而,当前的DNN是基于原始文本数据进行训练的,这限制了它们的生物学和认知可信性.
研究的目的:
- 评估DNN作为思想和大脑语言组织的科学模型.
- 突出当前DNN在捕捉语言获取和处理的复杂性方面的局限性.
主要方法:
- 概念分析比较DNN架构和训练与已建立的语言和认知理论.
- 关于语言发展,婴儿生物学和语言的自然原则的科学文献的审查.
主要成果:
- 尽管DNN在特定任务中取得了成功,但它缺乏强大的语言建模所需的生物学基础和发展考虑.
- 当前的模型没有考虑到先天的婴儿生物学,成熟,经验和自然规律在语言组织中的基本原则之间的相互作用.
结论:
- 目前,DNN作为对心灵和大脑语言系统的全面科学模型是不够的.
- 未来的模型必须整合生物学,发育和自然原则,以准确地表示语言组织.
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