论文标题
深度学习的句法结构
Syntactic Structure from Deep Learning
论文作者
论文摘要
现代深层神经网络在需要广泛的语言技能（例如机器翻译）的工程应用中实现了令人印象深刻的性能。这一成功引发了人们的兴趣，探索这些模型是否是从他们接触到的原始数据中引起人类的语法知识，因此，他们是否可以对有关语言获取必要的先天结构的长期辩论进行新的启示。在本文中，我们调查了深网的句法能力的代表性研究，并讨论了这项工作对理论语言学的更广泛含义。
Modern deep neural networks achieve impressive performance in engineering applications that require extensive linguistic skills, such as machine translation. This success has sparked interest in probing whether these models are inducing human-like grammatical knowledge from the raw data they are exposed to, and, consequently, whether they can shed new light on long-standing debates concerning the innate structure necessary for language acquisition. In this article, we survey representative studies of the syntactic abilities of deep networks, and discuss the broader implications that this work has for theoretical linguistics.
