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Modeling law search and retrieval as prediction problems has recently emergedas a predominant approach in law intelligence. Focusing on the law articleretrieval task, we present a deep learning framework named LamBERTa, which isdesigned for civil-law codes, and specifically trained on the Italian civilcode. To our knowledge, this is the first study proposing an advanced approachto law article prediction for the Italian legal system based on a BERT(Bidirectional Encoder Representations from Transformers) learning framework,which has recently attracted increased attention among deep learningapproaches, showing outstanding effectiveness in several natural languageprocessing and learning tasks. We define LamBERTa models by fine-tuning anItalian pre-trained BERT on the Italian civil code or its portions, for lawarticle retrieval as a classification task. One key aspect of our LamBERTaframework is that we conceived it to address an extreme classificationscenario, which is characterized by a high number of classes, the few-shotlearning problem, and the lack of test query benchmarks for Italian legalprediction tasks. To solve such issues, we define different methods for theunsupervised labeling of the law articles, which can in principle be applied toany law article code system. We provide insights into the explainability andinterpretability of our LamBERTa models, and we present an extensiveexperimental analysis over query sets of different type, for single-label aswell as multi-label evaluation tasks. Empirical evidence has shown theeffectiveness of LamBERTa, and also its superiority against widely useddeep-learning text classifiers and a few-shot learner conceived for anattribute-aware prediction task.

Unsupervised Law Article Mining based on Deep Pre-Trained Language Representation Models with Application to the Italian Civil Code