语音深度模型能更好连接语言学与人工智能。
Linguists should learn to love speech-based deep learning models
- 用语音模型替代文本模型，更贴近人类语言本质
- 语音数据能捕捉书面语忽略的语言现象
- 适合关注真实语言使用的语言学家和研究者
Futrell 和 Mahowald 提出一个连接技术导向的深度学习系统与解释导向的语言学理论的有用框架。然而，目标文章聚焦于生成式文本型大模型，从根本上限制了与语言学的深入互动，因为许多关于人类语言的重要问题无法在书面文本中体现。我们主张，基于音频的深度学习模型应发挥关键作用。
原文摘要 · Abstract (English)
Futrell and Mahowald present a useful framework bridging technology-oriented deep learning systems and explanation-oriented linguistic theories. Unfortunately, the target article's focus on generative text-based LLMs fundamentally limits fruitful interactions with linguistics, as many interesting questions on human language fall outside what is captured by written text. We argue that audio-based deep learning models can and should play a crucial role.
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