基于深度预训练语言表示模型的无监督法律文章挖掘及其在意大利民法典中的应用
Unsupervised Law Article Mining based on Deep Pre-Trained Language Representation Models with Application to the Italian Civil Code
摘要：
将法律搜索和检索建模为预测问题是近年来法律智能研究中的一种主流方法。专注于法律文章检索任务，我们提出了一个名为LamBERTa的深度学习框架，该框架专为民法法典设计，并专门针对意大利民法典进行了培训。据我们所知，这是第一项基于BERT（来自Transformers的双向编码器表示）学习框架为意大利法律系统提出法律文章预测的高级方法的研究，该框架最近在深度学习方法中引起了越来越多的关注，在几个自然语言处理和学习任务中表现出了出色的效果。我们定义LamBERTa模型微调意大利预训练BERT的意大利民法典或其部分，作为一个分类任务的法律文章检索。我们的LamBERTa框架的一个关键方面是，我们设想它来解决一个极端的分类方案，其特点是大量的类，少样本学习问题，以及缺乏测试查询基准意大利法律预测任务。为了解决这些问题，我们定义了不同的方法来对法律条文进行无监督标注，这些方法原则上可以适用于任何法律条文代码系统。我们提供了我们的LamBERTa模型的可解释性和可解释性的见解，我们提出了一个广泛的实验分析不同类型的查询集，单标签以及多标签评估任务。经验证据表明了LamBERTa的有效性，以及它对广泛使用的深度学习文本分类器和为属性感知预测任务设计的Few-Shot学习器的优越性。
英文摘要：
Modeling law search and retrieval as prediction problems has recently emerged as a predominant approach in law intelligence. Focusing on the law article retrieval task, we present a deep learning framework named LamBERTa, which is designed for civil-law codes, and specifically trained on the Italian civil code. To our knowledge, this is the first study proposing an advanced approach to law article prediction for the Italian legal system based on a BERT (Bidirectional Encoder Representations from Transformers) learning framework, which has recently attracted increased attention among deep learning approaches, showing outstanding effectiveness in several natural language processing and learning tasks. We define LamBERTa models by fine-tuning an Italian pre-trained BERT on the Italian civil code or its portions, for law article retrieval as a classification task. One key aspect of our LamBERTa framework is that we conceived it to address an extreme classification scenario, which is characterized by a high number of classes, the few-shot learning problem, and the lack of test query benchmarks for Italian legal prediction tasks. To solve such issues, we define different methods for the unsupervised labeling of the law articles, which can in principle be applied to any law article code system. We provide insights into the explainability and interpretability of our LamBERTa models, and we present an extensive experimental analysis over query sets of different type, for single-label as well as multi-label evaluation tasks. Empirical evidence has shown the effectiveness of LamBERTa, and also its superiority against widely used deep-learning text classifiers and a few-shot learner conceived for an attribute-aware prediction task.
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