  A substantial thread of recent work on latent tree learning has attempted to
develop neural network models with parse-valued latent variables and train them
on non-parsing tasks, in the hope of having them discover interpretable tree
structure. In a recent paper, Shen et al. (2018) introduce such a model and
report near-state-of-the-art results on the target task of language modeling,
and the first strong latent tree learning result on constituency parsing. In an
attempt to reproduce these results, we discover issues that make the original
results hard to trust, including tuning and even training on what is
effectively the test set. Here, we attempt to reproduce these results in a fair
experiment and to extend them to two new datasets. We find that the results of
this work are robust: All variants of the model under study outperform all
latent tree learning baselines, and perform competitively with symbolic grammar
induction systems. We find that this model represents the first empirical
success for latent tree learning, and that neural network language modeling
warrants further study as a setting for grammar induction.
