  Modeling law search and retrieval as prediction problems has recently emerged
as a predominant approach in law intelligence. Focusing on the law article
retrieval task, we present a deep learning framework named LamBERTa, which is
designed for civil-law codes, and specifically trained on the Italian civil
code. To our knowledge, this is the first study proposing an advanced approach
to 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 learning
approaches, showing outstanding effectiveness in several natural language
processing and learning tasks. We define LamBERTa models by fine-tuning an
Italian pre-trained BERT on the Italian civil code or its portions, for law
article retrieval as a classification task. One key aspect of our LamBERTa
framework is that we conceived it to address an extreme classification
scenario, which is characterized by a high number of classes, the few-shot
learning problem, and the lack of test query benchmarks for Italian legal
prediction tasks. To solve such issues, we define different methods for the
unsupervised labeling of the law articles, which can in principle be applied to
any law article code system. We provide insights into the explainability and
interpretability of our LamBERTa models, and we present an extensive
experimental analysis over query sets of different type, for single-label as
well as multi-label evaluation tasks. Empirical evidence has shown the
effectiveness of LamBERTa, and also its superiority against widely used
deep-learning text classifiers and a few-shot learner conceived for an
attribute-aware prediction task.
