Few Shot Argument Mining

This is the repository for the ACL 2024 paper Argument Mining in Data Scarce Settings: Cross-lingual Transfer and Few-shot Techniques

In the paper, we explore different strategies for dealing with data scarcity in Argument Mining tasks, namely fine-tuning multilingual BERT and adapting EntLM (a template-free few-shot approach for sequence labeling task). In our experiments, we generate the few-shot medical data from the AbstRCT corpus in 4 languages (English, Spanish, Italian and French).

Usage

1. Run pip install requirements to install the required packages

Data

All the data used for the experiments can be found in dataset folder.

EntLM

1. Run sh scripts/count_freq.sh to generate the label words for EntLM
2. Run sh scripts/run_fewshot.sh to launch few-shot learning using EntLM

Fine-tuning mBERT

1. Run sh fine-tuning/finetune_fewshot.sh to fine-tune the model with a small amount of data

• Alternatively, you can run sh fine-tuning/finetune_full.sh in order to fine-tune the model using full data.

Citation

@article{yeginbergen2024argument,
  title={Argument Mining in Data Scarce Settings: Cross-lingual Transfer and Few-shot Techniques},
  author={Yeginbergen, Anar and Oronoz, Maite and Agerri, Rodrigo},
  journal={arXiv preprint arXiv:2407.03748},
  year={2024}
}