Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning

PyTorch implementation of the Reinforcement Learning for Distant Supervision RE model described in our ACL 2018 paper Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning. In this work, we try to use reinforcement learning method to detect and remove noise instances for each relation type; moreover, this process is independent to the traning of relation extraction system.

Steps to run the experiments

Requirements

Python 2.7.12
PyTorch 0.4.1
panda 0.19.1

Datasets and word embeddings

Dataset and Pretrained word embeddings are from OpenNRE. Please download(Baidu Yun or Google Drive) and put it into this directory.
We include two versions of training dataset; they have different size, 522611 sentences and 570088 sentences repectively. This two options are included in args.py. Compared with 570088 version, 522611 version removes entity pairs that are repetitive with test dataset. 522611 is the default options in args.py.

Training

python train.py

Output

The cleaned dataset is outputed to the directory ./cleaned_data.

Test

In order to validate the performance, we run thunlp/NRE on the cleaned dataset. For convenience, we have put their code in to the directory ./NRE-master.
Taking CNN-ONE model as an example, run the code by
make
./train
The Precision-Recall file is outputed to ./NRE-master/CNN-ONE/out. Good Precision-Recal curves can be obtained from pr11.txt to pr14.txt.

Plot

plot_PR_curve.ipynb

Reference

@article{qin2018robust,
  title={Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning},
  author={Qin, Pengda and Xu, Weiran and Wang, William Yang},
  journal={arXiv preprint arXiv:1805.09927},
  year={2018}
}

Panda0406 / Reinforcement-Learning-Distant-Supervision-RE