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VGCN-BERT: Augmenting BERT with Graph Embedding for Text Classification

Authors: Zhibin Lu (zhibin.lu@umontreal.ca), Pan Du (pan.du@umontreal.ca), Jian-Yun Nie (nie@iro.umontreal.ca)

Overview

This is the implementation of our ECIR 2020 paper: "VGCN-BERT: Augmenting BERT with Graph Embedding for Text Classification".

If you make use of this code or the VGCN-BERT approach in your work, please cite the following paper:

 @inproceedings{ZhibinluGraphEmbedding,
     author    = {Zhibin Lu and Pan Du and Jian-Yun Nie},
     title     = {VGCN-BERT: Augmenting BERT with Graph Embedding for Text Classification},
     booktitle = {Advances in Information Retrieval - 42nd European Conference on {IR}
                       Research, {ECIR} 2020, Lisbon, Portugal, April 14-17, 2020, Proceedings,
                       Part {I}},
     series    = {Lecture Notes in Computer Science},
     volume    = {12035},
     pages     = {369--382},
     publisher = {Springer},
     year      = {2020},
   }

Requirements

Directory Structure

The data/ directory contains the original datasets and the corresponding pre-processed data. The output/ directory saves the model files while training the models. The pytorch_pretrained_bert/ directory is the module of Huggingface transformer. The other files with the .py suffix are models, utility functions, training programs, and prediction programs.

Running the code

Pre-processing datasets

Run prepare_data.py to pre-process the dataset and generate the vocabulary graph.

Examples:

python prepare_data.py --ds cola

Train models

Run train_vanilla_vgcn_bert.py to train the Vanilla-VGCN-BERT model. Before training the model, you must ensure that you have pre-processed the dataset.

Examples:

python train_vanilla_vgcn_bert.py --ds cola

Run train_vgcn_bert.py to train the VGCN-BERT model. Before training the model, you must ensure that you have pre-processed the dataset.

Examples:

python train_vgcn_bert.py --ds cola

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