@inproceedings{grundler-etal-2024-amelia,
    title = "{AMELIA} - Argument Mining Evaluation on Legal documents in {I}t{A}lian: A {CALAMITA} Challenge",
    author = "Grundler, Giulia  and
      Galassi, Andrea  and
      Santin, Piera  and
      Fidelangeli, Alessia  and
      Galli, Federico  and
      Palmieri, Elena  and
      Lagioia, Francesca  and
      Sartor, Giovanni  and
      Torroni, Paolo",
    editor = "Dell'Orletta, Felice  and
      Lenci, Alessandro  and
      Montemagni, Simonetta  and
      Sprugnoli, Rachele",
    booktitle = "Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)",
    month = dec,
    year = "2024",
    address = "Pisa, Italy",
    publisher = "CEUR Workshop Proceedings",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.clicit-1.124/",
    pages = "1125--1134",
    ISBN = "979-12-210-7060-6",
    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."
}