@inproceedings{Zhang2023,
 title = {Argument {{Mining}} with {{Graph Representation Learning}}},
 booktitle = {Proceedings of the {{Nineteenth International Conference}} on {{Artificial Intelligence}} and {{Law}}},
 author = {Zhang, Gechuan and Nulty, Paul and Lillis, David},
 year = {2023},
 month = {September},
 series = {{{ICAIL}} '23},
 pages = {371--380},
 publisher = {{Association for Computing Machinery}},
 address = {{New York, NY, USA}},
 doi = {10.1145/3594536.3595152},
 url = {https://dl.acm.org/doi/10.1145/3594536.3595152},
 urldate = {2023-09-13},
 abstract = {Argument Mining (AM) is a unique task in Natural Language Processing (NLP) that targets arguments: a meaningful logical structure in human language. Since the argument plays a significant role in the legal field, the interdisciplinary study of AM on legal texts has significant promise. For years, a pipeline architecture has been used as the standard paradigm in this area. Although this simplifies the development and management of AM systems, the connection between different parts of the pipeline causes inevitable shortcomings such as cascading error propagation. This paper presents an alternative perspective of the AM task, whereby legal documents are represented as graph structures and the AM task is undertaken as a hybrid approach incorporating Graph Neural Networks (GNNs), graph augmentation and collective classification. GNNs have been demonstrated to be an effective method for representation learning on graphs, and they have been successfully applied to many other NLP tasks. In contrast to previous pipeline-based architecture, our approach results in a single end-to-end classifier for the identification and classification of argumentative text segments. Experiments based on corpora from both the European Court of Human Rights (ECHR) and the Court of Justice of the European Union (CJEU) show that our approach achieves strong results compared to state-of-the-art baselines. Both the graph augmentation and collective classification steps are shown to improve performance on both datasets when compared to using GNNs alone.},
 isbn = {9798400701979},
 keywords = {Argument Mining,Graph Neural Networks,Legal Text},
}