A Bayesian Model for Generative Transition-based Dependency Parsing

By Jan Buys, Phil Blunsom. 2015

10 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Recurrent Neural Network Grammars (2016, background): “Dependency-only language models have also been explored (Titov and Henderson, 2007; Buys and Blunsom, 2015a,b).”
Generative Incremental Dependency Parsing with Neural Networks (2015, methodology): “We perform efﬁcient linear-time decoding with a particle ﬁltering-based beam-search method where derivations after pruned after every word generation and the beam size depends on the uncertainty in the model (Buys and Blunsom, 2015).”