Papers › A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing
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
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Part-Of-Speech Tagging | UD | Joint Bi-LSTM | Avg accuracy | 95.55 | #5 of 5 | Archive leaderboard | report |
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