PLMs_GNNs_in_NLP/mhgcn

• Multi-hop Graph Convolutional Network (MultiHopGCN)
• • Requirements
  • Usage
  • Citation

Multi-hop Graph Convolutional Network (MultiHopGCN)

The code and dataset for the ACL2021 paper Multi-hop Graph Convolutional Network with High-order Chebyshev Approximation for Text Reasoning, implemented in PyTorch.

Some functions are based on Text GCN. Thank for their work.

Requirements

• Python >=3.6
• PyTorch >=1.12.0
• Scipy 1.5.1

Usage

Download pre-trained word embeddings glove.6B.300d.txt from here and unzip to the root file.

First, use the remove_words.py to clean up the original text in datasets

python remove_words.py [DATASET]

Build graphs from the datasets in data/ as:

python build_fixed_graph.py [DATASET] [WINSIZE]

Provided datasets include ohsumed. The default sliding window size is 5 with weighted connection.

To use your own dataset, put the text file under data/ and the label file under data/ as other datasets do. Preprocess the text by running remove_words.py before building the graphs.

Start training and inference as:

python train.py [--dataset ohsumed]

Citation

@inproceedings{
    jiang2021multi,
    title={Multi-hop Graph Convolutional Network with High-order Chebyshev Approximation for Text Reasoning},
    author={Jiang, Shuoran and Chen, Qingcai and Liu, Xin and Hu, Baotian and Zhang, Lisai},
    booktitle={Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
    pages={6563--6573},
    year={2021}
}