@InProceedings{nguyen-dras-johnson:2017:K17-3,
  author    = {Nguyen, Dat Quoc  and  Dras, Mark  and  Johnson, Mark},
  title     = {A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing},
  booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {134--142},
  abstract  = {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},
  url       = {http://www.aclweb.org/anthology/K17-3014}
}