/AGGCN_TACRED

Attention Guided Graph Convolutional Networks for Relation Extraction (authors' PyTorch implementation for the ACL19 paper)

Primary LanguagePythonMIT LicenseMIT

Attention Guided Graph Convolutional Networks for Relation Extraction

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:

AGGCN Architecture

Requirements

Our model was trained on GPU Tesla P100-SXM2 of Nvidia DGX.

  • Python 3 (tested on 3.6.8)

  • PyTorch (tested on 0.4.1)

  • CUDA (tested on 9.0)

  • tqdm

  • unzip, wget (for downloading only)

Preparation

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.

Training

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.

Evaluation

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.

Retrain

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

Related Repo

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.

Citation

@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}
}