A Novel Neural Network Model for Joint POS Tagging and Graph based Dependency Parsing
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 network based 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
CoNLL Shared Task