Second-Order Unsupervised Neural Dependency Parsing

Understand

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.