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.
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.
Federico Ruggeri
Postdoctoral Research Fellow
His research aims to devise Natural Language Processing (NLP) systems that learn to generate, distill, and use knowledge from unstructured text.
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.