  Neural network architectures have been augmented with differentiable stacks
in order to introduce a bias toward learning hierarchy-sensitive regularities.
It has, however, proven difficult to assess the degree to which such a bias is
effective, as the operation of the differentiable stack is not always
interpretable. In this paper, we attempt to detect the presence of latent
representations of hierarchical structure through an exploration of the
unsupervised learning of constituency structure. Using a technique due to Shen
et al. (2018a,b), we extract syntactic trees from the pushing behavior of stack
RNNs trained on language modeling and classification objectives. We find that
our models produce parses that reflect natural language syntactic
constituencies, demonstrating that stack RNNs do indeed infer linguistically
relevant hierarchical structure.
