Abstract
Recently, there has been an increasing interest in unsupervised parsers that optimize semantically oriented objectives, typically using reinforcement learning. Unfortunately, the
learned trees often do not match actual syntax trees well. Shen et al. (2018) propose a
structured attention mechanism for language
modeling (PRPN), which induces better syntactic structures but relies on ad hoc heuristics.
Also, their model lacks interpretability as it is
not grounded in parsing actions. In our work,
we propose an imitation learning approach to
unsupervised parsing, where we transfer the
syntactic knowledge induced by the PRPN to
a Tree-LSTM model with discrete parsing actions. Its policy is then refined by GumbelSoftmax training towards a semantically oriented objective. We evaluate our approach
on the All Natural Language Inference dataset
and show that it achieves a new state of the
art in terms of parsing F-score, outperforming
our base models, including the PRPN.1