Dependency grammar induction is the task of learning dependency syntax
without annotated training data. Traditional graph-based models with global
inference achieve state-of-the-art results on this task but they require
$O(n^3)$ run time. Transition-based models enable faster inference with $O(n)$
time complexity, but their performance still lags behind. In this work, we
propose a neural transition-based parser for dependency grammar induction,
whose inference procedure utilizes rich neural features with $O(n)$ time
complexity. We train the parser with an integration of variational inference,
posterior regularization and variance reduction techniques. The resulting
framework outperforms previous unsupervised transition-based dependency parsers
and achieves performance comparable to graph-based models, both on the English
Penn Treebank and on the Universal Dependency Treebank. In an empirical
comparison, we show that our approach substantially increases parsing speed
over graph-based models.

本研究提出了一种基于神经网络的转移句法解析器，利用变分推断、后验正则化和方差减小技术训练模型，实现了无监督依赖语法归纳并在英语 Penn Treebank 和 Universal Dependency Treebank 上取得了可以与基于图的模型相媲美的表现，并且在实验比较中显示出比基于图的模型更快的解析速度。