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.

中文翻译：

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法律文本序列分类中的可解释性方法

机器学习在法律领域的应用多种多样。为了对机器学习社区和法律社区做出贡献，我们努力创建一个兼容文本序列分类的模型，重视结果的可解释性。本文的目的是将巴西的法律程序分为三种可能的状态类别，即 (i) 已存档的程序、(ii) 进行中的程序和 (iii) 暂停的程序。尽管使用葡萄牙语 NLP 由于缺乏资源而很难，但我们的方法在分类任务中表现非常出色。此外，除了量化这些模式与分类任务的关系之外，我们还能够提取和解释神经网络学习的模式。