  In recent years,the entire field of Natural Language Processing (NLP) has
enjoyed amazing novel results achieving almost human-like performance on a
variety of tasks. Legal NLP domain has also been part of this process, as it
has seen an impressive growth. However, general-purpose models are not readily
applicable for legal domain. Due to the nature of the domain (e.g. specialized
vocabulary, long documents) specific models and methods are often needed for
Legal NLP. In this work we investigate both specialized and general models for
predicting the final ruling of a legal case, task known as Legal Judgment
Prediction (LJP). We particularly focus on methods to extend to sequence length
of Transformer-based models to better understand the long documents present in
legal corpora. Extensive experiments on 4 LJP datasets in Romanian, originating
from 2 sources with significantly different sizes and document lengths, show
that specialized models and handling long texts are critical for a good
performance.
