In this paper, we attempt to link the inner workings of a neural language
model to linguistic theory, focusing on a complex phenomenon well discussed in
formal linguis- tics: (negative) polarity items. We briefly discuss the leading
hypotheses about the licensing contexts that allow negative polarity items and
evaluate to what extent a neural language model has the ability to correctly
process a subset of such constructions. We show that the model finds a relation
between the licensing context and the negative polarity item and appears to be
aware of the scope of this context, which we extract from a parse tree of the
sentence. With this research, we hope to pave the way for other studies linking
formal linguistics to deep learning.

本文试图将神经语言模型的内部工作与语言学理论联系起来，特别关注了形式语言学中广泛讨论的复杂现象（负）极性项，并评估了神经语言模型处理这种构造的能力。我们展示了模型在许可环境和负极性项之间找到了一种关系，并且似乎意识到了这种环境的范围。通过这项研究，我们希望为其他将形式语言学与深度学习相结合的研究铺平道路。