Few-Shot Learning for Argument Aspects of the Nuclear Energy Debate

This repository contains the experiment code for the paper:

Jurkschat, L., Wiedemann, G., Heinrich, M., Ruckdeschel, M., & Torge, S. (2022). Few-Shot Learning for Argument Aspects of the Nuclear Energy Debate. In Proceedings of the 13th International Conference on Language Resources and Evaluation (LREC 2022). European Language Resources Association (ELRA).

Experiments:

1. Evaluation of different pre-trained transformer models to classify the AAC-NE dataset [1]
2. Evaluation of two few-shot learning approaches to argument aspect mining
3. Application of the best few-shot learner to a newspaper corpus of argumentative sentences from "The Guardian"

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[1] Jurkschat, L., Wiedemann, G., Heinrich, M., Ruckdeschel, M., & Torge, S. (2022). Argument Aspect Corpus - Nuclear Energy. https://doi.org/10.5281/zenodo.6470232