Argument structure prediction aims to identify the relations between arguments or between parts of arguments. It is a crucial task in legal argument mining, where it could help identifying motivations behind judgments or even fallacies or inconsistencies. It is also a very challenging task, which is relatively underdeveloped compared to other argument mining tasks, owing to a number of reasons including a low availability of datasets and a high complexity of the reasoning involved. In this work, we address argumentative link prediction in decisions by Court of Justice of the European Union on fiscal state aid. We study how propositions are combined in higher-level structures and how the relations between propositions can be predicted by NLP models. To this end, we present a novel annotation scheme and use it to extend a dataset from literature with an additional annotation layer. We use our new dataset to run an empirical study, where we compare two architectures and explore different combinations of hyperparameters and training regimes. Our results indicate that an ensemble of residual networks yields the best results.

Piera Santin, G.G. (2023). Argumentation Structure Prediction in CJEU Decisions on Fiscal State Aid. New York : ACM [10.1145/3594536.3595174].

Argumentation Structure Prediction in CJEU Decisions on Fiscal State Aid

Piera Santin
Co-primo
;
Giulia Grundler
Co-primo
;
Andrea Galassi
;
Federico Galli;Francesca Lagioia
;
Elena Palmieri;Federico Ruggeri;Giovanni Sartor;Paolo Torroni
2023

Abstract

Argument structure prediction aims to identify the relations between arguments or between parts of arguments. It is a crucial task in legal argument mining, where it could help identifying motivations behind judgments or even fallacies or inconsistencies. It is also a very challenging task, which is relatively underdeveloped compared to other argument mining tasks, owing to a number of reasons including a low availability of datasets and a high complexity of the reasoning involved. In this work, we address argumentative link prediction in decisions by Court of Justice of the European Union on fiscal state aid. We study how propositions are combined in higher-level structures and how the relations between propositions can be predicted by NLP models. To this end, we present a novel annotation scheme and use it to extend a dataset from literature with an additional annotation layer. We use our new dataset to run an empirical study, where we compare two architectures and explore different combinations of hyperparameters and training regimes. Our results indicate that an ensemble of residual networks yields the best results.
2023
ICAIL '23: Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law
247
256
Piera Santin, G.G. (2023). Argumentation Structure Prediction in CJEU Decisions on Fiscal State Aid. New York : ACM [10.1145/3594536.3595174].
Piera Santin, Giulia Grundler, Andrea Galassi, Federico Galli, Francesca Lagioia, Elena Palmieri, Federico Ruggeri, Giovanni Sartor, Paolo Torroni
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/926658
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