Multi-Label Classification (MLC) is a common task in the legal domain, where more than one label may be assigned to a legal document. A wide range of methods can be applied, ranging from traditional ML approaches to the latest Transformer-based architectures. In this work, we perform an evaluation of different MLC methods using two public legal datasets, POSTURE50K and EURLEX57K. By varying the amount of training data and the number of labels, we explore the comparative advantage offered by different approaches in relation to the dataset properties. Our findings highlight DistilRoBERTa and LegalBERT as performing consistently well in legal MLC with reasonable computational demands. T5 also demonstrates comparable performance while offering advantages as a generative model in the presence of changing label sets. Finally, we show that the CrossEncoder exhibits potential for notable macro-F1 score improvements, albeit with increased computational costs.

翻译：多标签分类（MLC）是法律领域中的常见任务，其中法律文档可能被分配多个标签。从传统机器学习方法到最新的基于Transformer的架构，多种方法均可适用。在本研究中，我们使用两个公开法律数据集POSTURE50K和EURLEX57K，对不同MLC方法进行了评估。通过改变训练数据量及标签数量，我们探讨了不同方法在数据集属性方面提供的相对优势。研究结果突显了DistilRoBERTa和LegalBERT在法律MLC中表现稳定，且计算需求合理。T5也展现出可比性能，同时在标签集变化的情况下作为生成模型具有优势。最后，我们表明CrossEncoder在宏F1分数上具有显著提升潜力，尽管计算成本有所增加。