Interpretable Approach in the Classification of Sequences of Legal Texts

Machine learning applications in the legal field are numerous and diverse. In order to make contribution to both the machine learning community and the legal community, we have made efforts to create a model compatible with the classification of text sequences, valuing the interpretability of the results. The purpose of this paper is to classify Brazilian legal proceedings in three possible status classes, which are (i) archived proceedings, (ii) active proceedings and (iii) suspended proceedings. Although working with portuguese NLP, which can be hard due to lack of resources, our approach performed remarkably well in the classification task. Furthermore, we were able to extract and interpret the patterns learnt by the neural network besides quantifying how those patterns relate to the classification task.