Detecting Arguments in CJEU Decisions on Fiscal State Aid

Giulia Grundler, Piera Santin, Andrea Galassi, Federico Galli, Francesco Godano, Francesca Lagioia, Elena Palmieri, Federico Ruggeri, Giovanni Sartor, Paolo Torroni


Abstract
The successful application of argument mining in the legal domain can dramatically impact many disciplines related to law. For this purpose, we present Demosthenes, a novel corpus for argument mining in legal documents, composed of 40 decisions of the Court of Justice of the European Union on matters of fiscal state aid. The annotation specifies three hierarchical levels of information: the argumentative elements, their types, and their argument schemes. In our experimental evaluation, we address 4 different classification tasks, combining advanced language models and traditional classifiers.
Anthology ID:
2022.argmining-1.14
Volume:
Proceedings of the 9th Workshop on Argument Mining
Month:
October
Year:
2022
Address:
Online and in Gyeongju, Republic of Korea
Venue:
ArgMining
SIG:
Publisher:
International Conference on Computational Linguistics
Note:
Pages:
143–157
Language:
URL:
https://aclanthology.org/2022.argmining-1.14
DOI:
Bibkey:
Cite (ACL):
Giulia Grundler, Piera Santin, Andrea Galassi, Federico Galli, Francesco Godano, Francesca Lagioia, Elena Palmieri, Federico Ruggeri, Giovanni Sartor, and Paolo Torroni. 2022. Detecting Arguments in CJEU Decisions on Fiscal State Aid. In Proceedings of the 9th Workshop on Argument Mining, pages 143–157, Online and in Gyeongju, Republic of Korea. International Conference on Computational Linguistics.
Cite (Informal):
Detecting Arguments in CJEU Decisions on Fiscal State Aid (Grundler et al., ArgMining 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.argmining-1.14.pdf
Code
 adele-project/demosthenes
Data
Demosthenes