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svjan5
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Tutorial: Graph Neural Networks for Natural Language Processing at EMNLP 2019 and CODS-COMAD 2020

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Graph Neural Networks for Natural Language Processing

Conference Conference Slides Colab

The repository contains code examples for GNN-for-NLP tutorial at EMNLP 2019 and CODS-COMAD 2020.

Slides can be downloaded from here.

Dependencies

  • Compatible with PyTorch 1.x, TensorFlow 1.x and Python 3.x.
  • Dependencies can be installed using
    requirements.txt
    .

TensorFlow Examples:

  • tf_gcn.py
    contains simplified implementation of first-order approximation of GCN model proposed by Kipf et. al. (2016)
  • Extensions of the same implementation for different problems:

PyTorch Examples:

  • pytorch_gcn.py
    is pytorch equivalent of
    tf_gcn.py
    implemented using pytorch-geometric.
  • Several other examples are available here.

Additional Resources:

Citation:

@inproceedings{vashishth-etal-2019-graph,
    title = "Graph-based Deep Learning in Natural Language Processing",
    author = "Vashishth, Shikhar  and
      Yadati, Naganand  and
      Talukdar, Partha",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): Tutorial Abstracts",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    abstract = "This tutorial aims to introduce recent advances in graph-based deep learning techniques such as Graph Convolutional Networks (GCNs) for Natural Language Processing (NLP). It provides a brief introduction to deep learning methods on non-Euclidean domains such as graphs and justifies their relevance in NLP. It then covers recent advances in applying graph-based deep learning methods for various NLP tasks, such as semantic role labeling, machine translation, relationship extraction, and many more.",
}

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