A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing
Dat Quoc Nguyen, Mark Dras, Mark Johnson · 2017
We present a novel neural network model that learns POS tagging and graph-based dependency parsing jointly.Our model uses bidirectional LSTMs to learn feature representations shared for both POS tagging and dependency parsing tasks, thus handling the feature-engineering problem.Our extensive experiments, on 19 languages from the Universal Dependencies project, show that our model outperforms the state-of-the-art neural networkbased Stack-propagation model for joint POS tagging and transition-based dependency parsing, resulting in a new state of the art.Our code is open-source and available together with pre-trained models at: https://github.com/datquocnguyen/jPTDP.