语言建模的最新进展显著降低了文本分类任务对标注数据的需求。在无标注数据上预训练的基于Transformer的模型，其性能能够超过为每项任务从头训练的模型。然而，对于需要专家级标注人员的领域（如法律领域），微调此类模型所需的标注数据量仍然相当高。本文研究了优化使用小型标注数据集和大量无标注数据的最佳策略，并在法律领域执行一项包含50个预定义主题的分类任务。更具体地说，我们使用向巴西公共检察厅提出的诉求记录，旨在将描述分配到其中一个主题，而目前这项工作需要深厚的法律知识来手工填写。鉴于葡萄牙语（尤其是法律领域）可用资源较少，在这种场景下优化分类器性能的任务尤其具有挑战性。我们的结果表明，与BERT语言模型相比，逻辑回归和SVM等经典监督模型，以及随机森林和梯度提升集成模型，在结合word2vec提取的嵌入时取得了更好的性能。BERT在将模型自身架构作为分类器时表现出优越性能，在这方面超过了此前所有模型。最佳结果由无监督数据增强（Unsupervised Data Augmentation，UDA）获得，该方法联合使用BERT、数据增强和半监督学习策略，在上述任务中取得了80.7%的准确率。
英文摘要：
Recent advances in language modelling has significantly decreased the need of labelled data in text classification tasks. Transformer-based models, pre-trained on unlabeled data, can outmatch the performance of models trained from scratch for each task. However, the amount of labelled data need to fine-tune such type of model is still considerably high for domains requiring expert-level annotators, like the legal domain. This paper investigates the best strategies for optimizing the use of a small labeled dataset and large amounts of unlabeled data and perform a classification task in the legal area with 50 predefined topics. More specifically, we use the records of demands to a Brazilian Public Prosecutor's Office aiming to assign the descriptions in one of the subjects, which currently demands deep legal knowledge for manual filling. The task of optimizing the performance of classifiers in this scenario is especially challenging, given the low amount of resources available regarding the Portuguese language, especially in the legal domain. Our results demonstrate that classic supervised models such as logistic regression and SVM and the ensembles random forest and gradient boosting achieve better performance along with embeddings extracted with word2vec when compared to BERT language model. The latter demonstrates superior performance in association with the architecture of the model itself as a classifier, having surpassed all previous models in that regard. The best result was obtained with Unsupervised Data Augmentation (UDA), which jointly uses BERT, data augmentation, and strategies of semi-supervised learning, with an accuracy of 80.7% in the aforementioned task.
One Law, Many Languages: Benchmarking Multilingual Legal Reasoning for Judicial Support
University of Bern（伯尔尼大学）；University of Copenhagen（哥本哈根大学）；Bern University of Applied Sciences（伯尔尼应用科学大学）；University of Fribourg（弗里堡大学）；Stanford University（斯坦福大学）
Capturing Legal Reasoning Paths from Facts to Law in Court Judgments using Knowledge Graphs
Graduate School of Information Science and Technology, The University of Tokyo（东京大学信息科学与技术研究生院）；The Canon Institute for Global Studies（Canon全球研究机构）；Graduate Schools for Law and Politics, The University of Tokyo（东京大学法学与政治学研究生院）；TKC Corporation（TKC公司）
Legal Judgment Reimagined: PredEx and the Rise of Intelligent AI Interpretation in Indian Courts
Symbiosis Law School（symbiosis 法学院）；IISER Kolkata（印度科学教育与研究学院加尔各答分校）；Indian Institute of Technology Kanpur（坎普尔印度理工学院）；Manipal University Jaipur（斋浦尔马尼帕尔大学）
ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation
Indian Institute of Technology Kanpur（坎普尔印度理工学院）；Indian Institute of Science Education and Research Kolkata（加尔各答印度科学教育与研究学院）；West Bengal National University of Juridical Sciences（西孟加拉国立法科大学）
Analysing similarities between legal court documents using natural language processing approaches based on Transformers
TRT5 - Regional Labour Court of the 5th Region（第五地区地区劳工法院）；SENAI CIMATEC University Center（SENAI CIMATEC大学中心）；Surrey Institute for People-Centred AI（萨里以人为本人工智能研究所）；University of Surrey（萨里大学）
AgentsCourt: Building Judicial Decision-Making Agents with Court Debate Simulation and Legal Knowledge Augmentation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
LexGenie: Automated Generation of Structured Reports for European Court of Human Rights Case Law
School of Computation, Information, and Technology（计算、信息与技术学院；慕尼黑工业大学）；Technical University of Munich（国际与发展研究研究生院）；Graduate Institute of International and Development Studies
AgentCourt: Simulating Court with Adversarial Evolvable Lawyer Agents
Southern University of Science and Technology（南方科技大学）；Shenzhen Key Laboratory for High Performance Data Mining, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳高性能数据挖掘重点实验室，深圳先进技术研究所，中国科学院）；SIAT-DELI AI and Law Joint Lab（SIAT-DELI人工智能与法律联合实验室）；Shenzhen MSU-BIT University（深圳MSU-BIT大学）；UNSW Sydney（新南威尔士大学悉尼分校）；SUAT（南方科技大学）
