Syntactic Structure from Deep Learning

Modern deep neural networks achieve impressive performance in engineering applications that require extensive linguistic skills, such as machine translation. This success has sparked interest probing whether these models are inducing human-like grammatical knowledge from the raw data they exposed to and, consequently, can shed new light on long-standing debates concerning innate structure necessary for language acquisition. In this article, we survey representative studies of syntactic abilities and discuss broader implications work theoretical linguistics.