Most of the unsupervised dependency parsers are based on first-order
probabilistic generative models that only consider local parent-child
information. Inspired by second-order supervised dependency parsing, we
proposed a second-order extension of unsupervised neural dependency models that
incorporate grandparent-child or sibling information. We also propose a novel
design of the neural parameterization and optimization methods of the
dependency models. In second-order models, the number of grammar rules grows
cubically with the increase of vocabulary size, making it difficult to train
lexicalized models that may contain thousands of words. To circumvent this
problem while still benefiting from both second-order parsing and
lexicalization, we use the agreement-based learning framework to jointly train
a second-order unlexicalized model and a first-order lexicalized model.
Experiments on multiple datasets show the effectiveness of our second-order
models compared with recent state-of-the-art methods. Our joint model achieves
a 10% improvement over the previous state-of-the-art parser on the full WSJ
test set

我们提出了一种新的无监督神经依赖模型的二阶扩展，它可以包含祖父 - 子或兄弟信息，并使用基于一致性的学习框架联合训练一二阶无词汇化模型和一阶词汇化模型，实验表明我们的二阶模型在多个数据集上的效果优于最近的最先进方法，并且我们的联合模型在完整的 WSJ 测试集上取得了 10% 的改进。