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.

本文介绍了现代深度神经网络在机器翻译等需要广泛语言技能的工程应用中取得的卓越性能，探讨它们是否从接触的原始数据中引导出类似于人类语法知识的思想，并讨论这些工作对理论语言学的广泛影响。

深度学习中的句法结构

Syntactic Structure from Deep Learning

Distributional semantics provides multi-dimensional, graded, empirically
induced word representations that successfully capture many aspects of meaning
in natural languages, as shown in a large body of work in computational
linguistics; yet, its impact in theoretical linguistics has so far been
limited. This review provides a critical discussion of the literature on
distributional semantics, with an emphasis on methods and results that are of
relevance for theoretical linguistics, in three areas: semantic change,
polysemy and composition, and the grammar-semantics interface (specifically,
the interface of semantics with syntax and with derivational morphology). The
review aims at fostering greater cross-fertilization of theoretical and
computational approaches to language, as a means to advance our collective
knowledge of how it works.

该综述文章阐述了分布语义学在理论语言学领域的影响有限，但其成功地捕捉了自然语言诸多意义方面，并回顾了该领域在语义变化、一词多义及组合，以及语法 - 语义接口等领域的研究成果，旨在在理论和计算语言学之间进行更大规模的交叉授粉，以推进我们集体的语言知识。

分布语义学与语言学理论

Distributional Semantics and Linguistic Theory

Pater's target article builds a persuasive case for establishing stronger
ties between theoretical linguistics and connectionism (deep learning). This
commentary extends his arguments to semantics, focusing in particular on issues
of learning, compositionality, and lexical meaning.

该论文提出建立理论语言学和连接主义（深度学习）之间更紧密的联系的有力论据。通过扩展他的论点来关注语义学、学习、组合性和词汇含义等问题。