TARA

Source code for ACL 2023 paper: An AMR-based Link Prediction Approach for Document-level Event Argument Extraction.

🔧 How to use our code?

1. Dependencies

pip install git+https://github.com/fastnlp/fastNLP@dev0.8.0
pip install git+https://github.com/fastnlp/fitlog
conda install pytorch==1.9.0 torchvision==0.10.0 torchaudio==0.9.0 cudatoolkit=10.2 -c pytorch
pip install transformers==4.22.2
pip install dgl-cu102 dglgo -f https://data.dgl.ai/wheels/repo.html

2. Data Preprocessing

We provide the preprocessed data here, which can be downloaded and used directly.

If you need to preprocess data from text, please refer to data_processing.

3. Training

Please execute the command python src_x/train.py for RAMS or WikiEvents, separately. To make adjustments to hyperparameters, kindly refer to src_x/parse.py and implement any necessary modifications.

4. Evaluation

You can evaluate the trained model by running the following commands:

bash evaluate_rams.sh
bash evaluate_wikievents.sh

🥳 Citation

If you find our work useful, please cite our paper:

@inproceedings{DBLP:conf/acl/0004GHZQZ23,
  author       = {Yuqing Yang and
                  Qipeng Guo and
                  Xiangkun Hu and
                  Yue Zhang and
                  Xipeng Qiu and
                  Zheng Zhang},
  editor       = {Anna Rogers and
                  Jordan L. Boyd{-}Graber and
                  Naoaki Okazaki},
  title        = {An AMR-based Link Prediction Approach for Document-level Event Argument
                  Extraction},
  booktitle    = {Proceedings of the 61st Annual Meeting of the Association for Computational
                  Linguistics (Volume 1: Long Papers), {ACL} 2023, Toronto, Canada,
                  July 9-14, 2023},
  pages        = {12876--12889},
  publisher    = {Association for Computational Linguistics},
  year         = {2023},
  url          = {https://doi.org/10.18653/v1/2023.acl-long.720},
  doi          = {10.18653/v1/2023.acl-long.720},
  timestamp    = {Thu, 10 Aug 2023 12:35:57 +0200},
  biburl       = {https://dblp.org/rec/conf/acl/0004GHZQZ23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}