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.

在这项工作中，我们使用两个公共法律数据集 POSTURE50K 和 EURLEX57K，通过改变训练数据量和标签数量，评估了不同的多标签分类方法，结果显示 DistilRoBERTa 和 LegalBERT 在法律多标签分类中表现良好，而 T5 在生成模型中表现相当，并且 CrossEncoder 在提高宏 F1 分数方面具有潜力，尽管计算成本增加。

合适的模型：法律多标签分类基线的评估

The Right Model for the Job: An Evaluation of Legal Multi-Label  Classification Baselines

Meaningfully comparing language models is challenging with current
explanation methods. Current explanations are overwhelming for humans due to
large vocabularies or incomparable across models. We present TopEx, an
explanation method that enables a level playing field for comparing language
models via model-agnostic topics. We demonstrate how TopEx can identify
similarities and differences between DistilRoBERTa and GPT-2 on a variety of
NLP tasks.

本研究提出了一种名为 TopEx 的解释方法，该方法通过使用模型无关的主题实现了对比较语言模型的公正性，并演示了如何使用 TopEx 在各种 NLP 任务上识别 DistilRoBERTa 和 GPT-2 之间的相似之处和差异。