A Bayesian Model for Generative Transition-based Dependency Parsing

Understand

We propose a simple, scalable, fully generative model for transition-based dependency parsing with high accuracy.

• The model, parameterized by Hierarchical Pitman-Yor Processes, overcomes the limitations of previous generative models by allowing fast and accurate inference.
• We propose an efficient decoding algorithm based on particle filtering that can adapt the beam size to the uncertainty in the model while jointly predicting POS tags and parse trees.
• The UAS of the parser is on par with that of a greedy discriminative baseline.