  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.
