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

May 16, 2017 · Entered Twilight · 🏛 Conference on Computational Natural Language Learning

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Repo contents: License.txt, README.md, decoder.py, jPTDP.py, learner.py, mnnl.py, sample, utils.py, utils

Authors Dat Quoc Nguyen, Mark Dras, Mark Johnson arXiv ID 1705.05952 Category cs.CL: Computation & Language Citations 38 Venue Conference on Computational Natural Language Learning Repository https://github.com/datquocnguyen/jPTDP ⭐ 156 Last Checked 1 month ago

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