Paolo Torroni


pdf bib
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
Proceedings of the 9th Workshop on Argument Mining

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.

pdf bib
Multimodal Argument Mining: A Case Study in Political Debates
Eleonora Mancini | Federico Ruggeri | Andrea Galassi | Paolo Torroni
Proceedings of the 9th Workshop on Argument Mining

We propose a study on multimodal argument mining in the domain of political debates. We collate and extend existing corpora and provide an initial empirical study on multimodal architectures, with a special emphasis on input encoding methods. Our results provide interesting indications about future directions in this important domain.

pdf bib
Combining WordNet and Word Embeddings in Data Augmentation for Legal Texts
Sezen Perçin | Andrea Galassi | Francesca Lagioia | Federico Ruggeri | Piera Santin | Giovanni Sartor | Paolo Torroni
Proceedings of the Natural Legal Language Processing Workshop 2022

Creating balanced labeled textual corpora for complex tasks, like legal analysis, is a challenging and expensive process that often requires the collaboration of domain experts. To address this problem, we propose a data augmentation method based on the combination of GloVe word embeddings and the WordNet ontology. We present an example of application in the legal domain, specifically on decisions of the Court of Justice of the European Union.Our evaluation with human experts confirms that our method is more robust than the alternatives.

pdf bib
Fast Vocabulary Transfer for Language Model Compression
Leonidas Gee | Andrea Zugarini | Leonardo Rigutini | Paolo Torroni
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track

Real-world business applications require a trade-off between language model performance and size. We propose a new method for model compression that relies on vocabulary transfer. We evaluate the method on various vertical domains and downstream tasks. Our results indicate that vocabulary transfer can be effectively used in combination with other compression techniques, yielding a significant reduction in model size and inference time while marginally compromising on performance.


pdf bib
A Corpus for Multilingual Analysis of Online Terms of Service
Kasper Drawzeski | Andrea Galassi | Agnieszka Jablonowska | Francesca Lagioia | Marco Lippi | Hans Wolfgang Micklitz | Giovanni Sartor | Giacomo Tagiuri | Paolo Torroni
Proceedings of the Natural Legal Language Processing Workshop 2021

We present the first annotated corpus for multilingual analysis of potentially unfair clauses in online Terms of Service. The data set comprises a total of 100 contracts, obtained from 25 documents annotated in four different languages: English, German, Italian, and Polish. For each contract, potentially unfair clauses for the consumer are annotated, for nine different unfairness categories. We show how a simple yet efficient annotation projection technique based on sentence embeddings could be used to automatically transfer annotations across languages.


pdf bib
Cross-lingual Annotation Projection in Legal Texts
Andrea Galassi | Kasper Drazewski | Marco Lippi | Paolo Torroni
Proceedings of the 28th International Conference on Computational Linguistics

We study annotation projection in text classification problems where source documents are published in multiple languages and may not be an exact translation of one another. In particular, we focus on the detection of unfair clauses in privacy policies and terms of service. We present the first English-German parallel asymmetric corpus for the task at hand. We study and compare several language-agnostic sentence-level projection methods. Our results indicate that a combination of word embeddings and dynamic time warping performs best.


pdf bib
Argumentative Link Prediction using Residual Networks and Multi-Objective Learning
Andrea Galassi | Marco Lippi | Paolo Torroni
Proceedings of the 5th Workshop on Argument Mining

We explore the use of residual networks for argumentation mining, with an emphasis on link prediction. The method we propose makes no assumptions on document or argument structure. We evaluate it on a challenging dataset consisting of user-generated comments collected from an online platform. Results show that our model outperforms an equivalent deep network and offers results comparable with state-of-the-art methods that rely on domain knowledge.