  Deep learning sequence models have led to a marked increase in performance
for a range of Natural Language Processing tasks, but it remains an open
question whether they are able to induce proper hierarchical generalizations
for representing natural language from linear input alone. Work using
artificial languages as training input has shown that LSTMs are capable of
inducing the stack-like data structures required to represent context-free and
certain mildly context-sensitive languages---formal language classes which
correspond in theory to the hierarchical structures of natural language. Here
we present a suite of experiments probing whether neural language models
trained on linguistic data induce these stack-like data structures and deploy
them while incrementally predicting words. We study two natural language
phenomena: center embedding sentences and syntactic island constraints on the
filler--gap dependency. In order to properly predict words in these structures,
a model must be able to temporarily suppress certain expectations and then
recover those expectations later, essentially pushing and popping these
expectations on a stack. Our results provide evidence that models can
successfully suppress and recover expectations in many cases, but do not fully
recover their previous grammatical state.

Computation and Language