Generative Incremental Dependency Parsing with Neural Networks
Jan Buys and Phil Blunsom
Abstract
We propose a neural network model for scalable generative transition-based dependency parsing. A probability distribution over both sentences and transition sequences is parameterised by a feed-forward neural network. The model surpasses the accuracy and speed of previous generative dependency parsers, reaching 91.1% UAS. Perplexity results show a strong improvement over n-gram language models, opening the way to the efficient integration of syntax into neural models for language generation.
Book Title
Proceedings ACL−IJNLP (Short Papers)
Year
2015