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 Gumbel-Softmax 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.

我们提出了一种模仿学习的无监督句法分析方法，将 PRPN 诱导的句法知识转移到带有离散句法分析操作的 Tree-LSTM 模型上，并通过 Gumbel-Softmax 训练不断优化其策略以实现更好的语义目标。在 All-NLI 数据集上进行实验，证明我们的方法在句法得分上超过了基准模型，包括 PRPN。