This paper/code introduces the Attention Guided Graph Convolutional graph convolutional networks (AGGCNs) over dependency trees for the large scale sentence-level relation extraction task (TACRED).
See below for an overview of the model architecture:
Our model was trained on GPU Tesla P100-SXM2 of Nvidia DGX.
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Python 3 (tested on 3.6.8)
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PyTorch (tested on 0.4.1)
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CUDA (tested on 9.0)
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tqdm
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unzip, wget (for downloading only)
The code requires that you have access to the TACRED dataset (LDC license required). Once you have the TACRED data, please put the JSON files under the directory dataset/tacred.
First, download and unzip GloVe vectors:
chmod +x download.sh; ./download.sh
Then prepare vocabulary and initial word vectors with:
python3 prepare_vocab.py dataset/tacred dataset/vocab --glove_dir dataset/glove
This will write vocabulary and word vectors as a numpy matrix into the dir dataset/vocab.
To train the AGGCN model, run:
bash train_aggcn.sh 1
Model checkpoints and logs will be saved to ./saved_models/01.
For details on the use of other parameters, please refer to train.py.
Our pretrained model is saved under the dir saved_models/01. To run evaluation on the test set, run:
python3 eval.py saved_models/01 --dataset test
This will use the best_model.pt file by default. Use --model checkpoint_epoch_10.pt to specify a model checkpoint file.
Reload a pretrained model and finetune it, run:
python train.py --load --model_file saved_models/01/best_model.pt --optim sgd --lr 0.001
The paper uses the model DCGCN, for detail architecture please refer to the TACL19 paper Densely Connected Graph Convolutional Network for Graph-to-Sequence Learning. Codes are adapted from the repo of the EMNLP18 paper Graph Convolution over Pruned Dependency Trees Improves Relation Extraction.
@inproceedings{guo2019aggcn,
author = {Guo, Zhijiang and Zhang, Yan and Lu, Wei.},
booktitle = {Proc. of ACL},
title = {Attention Guided Graph Convolutional Networks for Relation Extraction},
year = {2019}
}