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Integrating graph embedding and neural models for improving transition-based dependency parsing

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Abstract

This paper introduces an effective method for improving dependency parsing which is based on a graph embedding model. The model helps extract local and global connectivity patterns between tokens. This method allows neural network models to perform better on dependency parsing benchmarks. We propose to incorporate node embeddings trained by a graph embedding algorithm into a bidirectional recurrent neural network scheme. The new model outperforms a baseline reference using a state-of-the-art method on three dependency treebanks for both low-resource and high-resource natural languages, namely Indonesian, Vietnamese and English. We also show that the popular pretraining technique of BERT would not pick up on the same kind of signal as graph embeddings. The new parser together with all trained models is made available under an open-source license, facilitating community engagement and advancement of natural language processing research for two low-resource languages with around 300 million users worldwide in total.

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Data availability

The datasets generated during and/or analyzed during the current study are available in the Universal Dependencies repository: https://universaldependencies.org.

Notes

  1. https://github.com/phuonglh/jvl/, under the VLP/aep module.

  2. https://universaldependencies.org/.

  3. https://spacy.io/api/dependencyparser.

  4. In practice, the dimensionality is usually in the range of a dozen to one thousand.

  5. https://www.internetworldstats.com/stats3.htm.

  6. Vietnamese Language and Speech Processing, http://vlsp.org.vn/.

  7. https://github.com/UniversalDependencies/UD_Vietnamese-VTB/.

  8. The shape dictionary of a word includes a dozen of different word forms such as number, date, allcaps, url....

  9. Kiperwasser and Goldberg [18] select the top three tokens on the stack and the first token on the buffer. They use the arc-hybrid system instead of the arc-eager system as in our work.

  10. The GSD treebank is about five times larger than the PUD or CSUI treebanks.

  11. All models are implemented in the Julia programming language using the https://fluxml.ai library.

  12. Recall that in the SOF variant, 20 embedding vectors of individual features are concatenated, resulting in an embedding dimension of \(20*e\).

  13. Kiperwasser and Goldberg [18] evaluated their model on the English Penn Treebank corpus.

  14. This small number makes sense due to a small number of 12 different possible word shapes.

  15. These embedding dimensions have been tuned by Kiperwasser and Goldberg [18].

  16. We use the package HypothesisTests of Julia to perform the statistical tests.

  17. More precisely, we use the model bert-uncased_L-12_H-768_A-12 which is publicly available.

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Acknowledgements

This study is supported by Vingroup Innovation Foundation (VINIF) in project code VINIF.2020.DA14.

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Authors and Affiliations

  1. Vietnam National University, Hanoi, Vietnam

    Phuong Le-Hong

  2. School of Computer Science and Engineering, NTU, Singapore, Singapore

    Erik Cambria

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Le-Hong, P., Cambria, E. Integrating graph embedding and neural models for improving transition-based dependency parsing. Neural Comput & Applic 36, 2999–3016 (2024). https://doi.org/10.1007/s00521-023-09223-3

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