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DICE: Data-Efficient Clinical Event Extraction with Generative Models

Source code and data for ACL 2023 main conference paper DICE: Data-Efficient Clinical Event Extraction with Generative Models.

Quick Start

# Training 
sh scripts/train.sh

# Evaluating a saved model
sh scritps/eval.sh

Use the following config file in the scripts for corresponding experiment:

• config/config_multitask_maccrobat_ET: train standalone mention identification module with slding window
• config/config_multitask_maccrobat_ET-ED: train event detection module with aux mention identification module and mention marker
• config/config_multitask_maccrobat_ET-EAE: train event argument extraction module with aux mention identification module and mention marker

Dataset: MACCROBAT-EE

Check maccrobat/Data folder for the entire event extraction dataset with argument annotation.

Environment

# Install conda environment
conda env create -f env.yml

Cite

@inproceedings{ma-etal-2023-dice,
    title = "DICE: Data-Efficient Clinical Event Extraction with Generative Models",
    author = "Ma, Mingyu Derek and Taylor, Alexander K. and Wang, Wei and Peng, Nanyun",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2208.07989",
}