@inproceedings{nguyen-etal-2017-novel,
title = "A Novel Neural Network Model for Joint {POS} Tagging and Graph-based Dependency Parsing",
author = "Nguyen, Dat Quoc and
Dras, Mark and
Johnson, Mark",
editor = "Haji\v c, Jan and
Zeman, Dan",
booktitle = "Proceedings of the {C}o{NLL} 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K17-3014/",
doi = "10.18653/v1/K17-3014",
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: \url{https://github.com/datquocnguyen/jPTDP}"
}
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%0 Conference Proceedings
%T A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing
%A Nguyen, Dat Quoc
%A Dras, Mark
%A Johnson, Mark
%Y Hajič, Jan
%Y Zeman, Dan
%S Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F nguyen-etal-2017-novel
%X 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
%R 10.18653/v1/K17-3014
%U https://aclanthology.org/K17-3014/
%U https://doi.org/10.18653/v1/K17-3014
%P 134-142
Markdown (Informal)
[A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing](https://aclanthology.org/K17-3014/) (Nguyen et al., CoNLL 2017)
ACL