  Recurrent Neural Networks (RNNs) trained on a language modeling task have
been shown to acquire a number of non-local grammatical dependencies with some
success. Here, we provide new evidence that RNN language models are sensitive
to hierarchical syntactic structure by investigating the filler--gap dependency
and constraints on it, known as syntactic islands. Previous work is
inconclusive about whether RNNs learn to attenuate their expectations for gaps
in island constructions in particular or in any sufficiently complex syntactic
environment. This paper gives new evidence for the former by providing control
studies that have been lacking so far. We demonstrate that two state-of-the-art
RNN models are are able to maintain the filler--gap dependency through
unbounded sentential embeddings and are also sensitive to the hierarchical
relationship between the filler and the gap. Next, we demonstrate that the
models are able to maintain possessive pronoun gender expectations through
island constructions---this control case rules out the possibility that island
constructions block all information flow in these networks. We also evaluate
three untested islands constraints: coordination islands, left branch islands,
and sentential subject islands. Models are able to learn left branch islands
and learn coordination islands gradiently, but fail to learn sentential subject
islands. Through these controls and new tests, we provide evidence that model
behavior is due to finer-grained expectations than gross syntactic complexity,
but also that the models are conspicuously un-humanlike in some of their
performance characteristics.
