Resumen de A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing

Dat Quoc Nguyen, Mark Dras, Mark Johnson

• 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 .