# data processed by dyieapp
data/text2target/dyiepp_ace1005_ettext2tri_subtype
├── event.schema 
├── test.json
├── train.json
└── val.json

# data processed by  data_convert.convert_text_to_target
data/text2target/dyiepp_ace1005_ettext2tri_subtype
├── event.schema
├── test.json
├── train.json
└── val.json

python -m data_convert.convert_text_to_target # data/raw_data -> data/text2target
python convert_dyiepp_to_sentence.py data/raw_data/dyiepp_ace2005 # doc -> sentence, used in evaluation

# ace05 event type detection t5-base, the metric_format use eval_trigger-F1 
bash run_seq2seq_span.bash --data=dyiepp_ace2005_text2et_subtype --model=t5-base --format=et --metric_format=eval_trigger-F1

# ace05 tri extraction t5-base
bash run_seq2seq_span.bash --data=dyiepp_ace2005_ettext2tri_subtype --model=t5-base --format=tri --metric_format=eval_trigger-F1

# ace05 argument extraction t5-base
bash run_seq2seq_span.bash --data=dyiepp_ace2005_etrttext2role_subtype --model=t5-base --format=role --metric_format=eval_role-F1

@misc{si2021-GDAP,
      title={Generating Disentangled Arguments with Prompts: A Simple Event Extraction Framework that Works}, 
      author={Jinghui Si and Xutan Peng and Chen Li and Haotian Xu and Jianxin Li},
      year={2021},
      eprint={2110.04525},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}