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。

神经变分转移句法分析中的依赖语法归纳

Dependency Grammar Induction with a Neural Variational Transition-based  Parser

We study the impact of big models (in terms of the degree of lexicalization)
and big data (in terms of the training corpus size) on dependency grammar
induction. We experimented with L-DMV, a lexicalized version of Dependency
Model with Valence and L-NDMV, our lexicalized extension of the Neural
Dependency Model with Valence. We find that L-DMV only benefits from very small
degrees of lexicalization and moderate sizes of training corpora. L-NDMV can
benefit from big training data and lexicalization of greater degrees,
especially when enhanced with good model initialization, and it achieves a
result that is competitive with the current state-of-the-art.

研究大模型和大数据对依存语法归纳的影响，发现 L-DMV 受益于很小的词汇化程度和适中的训练语料库大小，而 L-NDMV 在与较好的模型初始化相结合时，可以受益于大型训练数据和更高程度的词汇化，达到了与当前最先进技术竞争力相当的结果。