Source code and dataset for NAACL 2019 paper "Adversarial Training for Weakly Supervised Event Detection".

• python == 3.6.3
• pytorch == 0.4.1
• numpy == 1.15.2
• sklearn == 0.20.0
• pytorch-pretrained-bert == 0.2.0

Due to the licence issues, we cannot share the source ACE2005 dataset or the preprocessed data.

So we specify the data format in DataFormat.md and you can preprocess the data follow the format.

Put the preprocessed .npy data files in the same directory as the codes.

For the BERT models, download the Bert_base_uncase model in ../../BERT_CACHE.

Run python train.py in corresponding directory to train the model.

If you want to tune the hyper parameters, see the constant.py and change the parameters defined in the file.

If the codes help you, please cite the following paper:

Adversarial Training for Weakly Supervised Event Detection. Xiaozhi Wang, Xu Han, Zhiyuan Liu, Maosong Sun, Peng Li. NAACL-HLT 2019.