On Learning Bipolar Gradual Argumentation Semantics with Neural Networks

Caren Al Anaissy, Sandeep Suntwal, Mihai Surdeanu, Srdjan Vesic

2024

Abstract

Computational argumentation has evolved as a key area in artificial intelligence, used to analyze aspects of thinking, making decisions, and conversing. As a result, it is currently employed in a variety of real-world contexts, from legal reasoning to intelligence analysis. An argumentation framework is modelled as a graph where the nodes represent arguments and the edges of the graph represent relations (i.e., supports, attacks) between nodes. In this work, we investigate the ability of neural network methods to learn a gradual bipolar argumentation semantics, which allows for both supports and attacks. We begin by calculating the acceptability degrees for graph nodes. These scores are generated using Quantitative Argumentation Debate (QuAD) argumentation semantics. We apply this approach to two benchmark datasets: Twelve Angry Men and Debate-pedia. Using this data, we train and evaluate the performance of three benchmark architectures: Multilayer Perceptron (MLP), Graph Convolution Network (GCN), and Graph Attention Network (GAT) to learn the acceptability degree scores produced by the QuAD semantics. Our results show that these neural network methods can learn bipolar gradual argumentation semantics. The models trained on GCN architecture perform better than the other two architectures underscoring the importance of modelling argumentation graphs explicitly. Our software is publicly available at: https://github.com/clulab/icaart24-argumentation.

Paper Citation

in Harvard Style

Al Anaissy C., Suntwal S., Surdeanu M. and Vesic S. (2024). On Learning Bipolar Gradual Argumentation Semantics with Neural Networks. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 493-499. DOI: 10.5220/0012448300003636

in Bibtex Style

@conference{icaart24,
author={Caren Al Anaissy and Sandeep Suntwal and Mihai Surdeanu and Srdjan Vesic},
title={On Learning Bipolar Gradual Argumentation Semantics with Neural Networks},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2024},
pages={493-499},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012448300003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - On Learning Bipolar Gradual Argumentation Semantics with Neural Networks
SN - 978-989-758-680-4
AU - Al Anaissy C.
AU - Suntwal S.
AU - Surdeanu M.
AU - Vesic S.
PY - 2024
SP - 493
EP - 499
DO - 10.5220/0012448300003636
PB - SciTePress