A Bayesian Model for Generative Transition-based Dependency Parsing

2015/06/13 by Jan Buys, Phil Blunsom, Buys, Jan +1
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Speech Recognition and Synthesis #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1506.04334

openalex publication_date 2015/06/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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. As a language model, it obtains better perplexity than a n-gram model by performing semi-supervised learning over a large unlabelled corpus. We show that the model is able to generate locally and syntactically coherent sentences, opening the door to further applications in language generation.

Citations

• Structured language modeling
• Transition-Based Dependency Parsing with Stack Long Short-Term Memory
• Three Generative, Lexicalised Models for Statistical Parsing
• Structured Training for Neural Network Transition-Based Parsing

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