This challenge consists of three classification tasks, in the context of argument mining in the legal domain. The tasks are based on a dataset of 225 Italian decisions on Value Added Tax, annotated to identify and categorize argumentative text. The objective of the first task is to classify each argumentative component as premise or conclusion, while the second and third tasks aim at classifying the type of premise: legal vs factual, and its corresponding argumentation scheme. The classes are highly unbalanced, hence evaluation is based on the macro F1 score.
Grundler, G., Galassi, A., Santin, P., Fidelangeli, A., Galli, F., Palmieri, E., et al. (2024). AMELIA - Argument Mining Evaluation on Legal documents in ItAlian: A CALAMITA Challenge.
AMELIA - Argument Mining Evaluation on Legal documents in ItAlian: A CALAMITA Challenge
Giulia Grundler;Andrea Galassi;Piera Santin;Alessia Fidelangeli;Federico Galli;Elena Palmieri;Francesca Lagioia;Giovanni Sartor;Paolo Torroni
2024
Abstract
This challenge consists of three classification tasks, in the context of argument mining in the legal domain. The tasks are based on a dataset of 225 Italian decisions on Value Added Tax, annotated to identify and categorize argumentative text. The objective of the first task is to classify each argumentative component as premise or conclusion, while the second and third tasks aim at classifying the type of premise: legal vs factual, and its corresponding argumentation scheme. The classes are highly unbalanced, hence evaluation is based on the macro F1 score.| File | Dimensione | Formato | |
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