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.
Giulia Grundler
PhD Student
Her research concerns applying machine learning and natural language processing methods to multilingual legal analytics in the context of the European and national legal frameworks.
Andrea Galassi
Junior Assistant Professor
He is an expert in deep learning architectures for natural language processing.
Paolo Torroni
Associate Professor
Head of the Language Technologies lab. His main research focus is in artificial intelligence, and in particular natural language processing, multi-agent systems, and computational logics.