Confusing charge prediction is a challenging task in legal AI, which involves
predicting confusing charges based on fact descriptions. While existing charge
prediction methods have shown impressive performance, they face significant
challenges when dealing with confusing charges, such as Snatch and Robbery. In
the legal domain, constituent elements play a pivotal role in distinguishing
confusing charges. Constituent elements are fundamental behaviors underlying
criminal punishment and have subtle distinctions among charges. In this paper,
we introduce a novel From Graph to Word Bag (FWGB) approach, which introduces
domain knowledge regarding constituent elements to guide the model in making
judgments on confusing charges, much like a judge's reasoning process.
Specifically, we first construct a legal knowledge graph containing constituent
elements to help select keywords for each charge, forming a word bag.
Subsequently, to guide the model's attention towards the differentiating
information for each charge within the context, we expand the attention
mechanism and introduce a new loss function with attention supervision through
words in the word bag. We construct the confusing charges dataset from
real-world judicial documents. Experiments demonstrate the effectiveness of our
method, especially in maintaining exceptional performance in imbalanced label
distributions.

在法律人工智能中，混淆罪名预测是一项具有挑战性的任务，本文介绍了一种从图形到词袋（FWGB）的新方法，通过引入组成要素的领域知识以指导模型对混淆罪名进行判断，从而提高预测性能。

从图到词袋：将领域知识引入混乱电荷预测

From Graph to Word Bag: Introducing Domain Knowledge to Confusing Charge  Prediction

Named entity recognition (NER), a task that identifies and categorizes named
entities such as persons or organizations from text, is traditionally framed as
a multi-class classification problem. However, this approach often overlooks
the issues of imbalanced label distributions, particularly in low-resource
settings, which is common in certain NER contexts, like biomedical NER
(bioNER). To address these issues, we propose an innovative reformulation of
the multi-class problem as a one-vs-all (OVA) learning problem and introduce a
loss function based on the area under the receiver operating characteristic
curve (AUC). To enhance the efficiency of our OVA-based approach, we propose
two training strategies: one groups labels with similar linguistic
characteristics, and another employs meta-learning. The superiority of our
approach is confirmed by its performance, which surpasses traditional NER
learning in varying NER settings.

提出了一个创新的方法，将多类问题作为一对多 (OVA) 学习问题来解决，引入了基于接收器操作特征曲线 (AUC) 下面积的损失函数，通过提出两种训练策略，改进了效率，从而在不同的命名实体识别 (NER) 设置中超过传统 NER 学习的性能。

低资源命名实体识别：一对多 AUC 最大化是否有帮助？

Low-Resource Named Entity Recognition: Can One-vs-All AUC Maximization  Help?

Using deep learning, we now have the ability to create exceptionally good
semantic segmentation systems; however, collecting the prerequisite pixel-wise
annotations for training images remains expensive and time-consuming.
Therefore, it would be ideal to minimize the number of human annotations needed
when creating a new dataset. Here, we address this problem by proposing a novel
algorithm that combines active learning and semi-supervised learning. Active
learning is an approach for identifying the best unlabeled samples to annotate.
While there has been work on active learning for segmentation, most methods
require annotating all pixel objects in each image, rather than only the most
informative regions. We argue that this is inefficient. Instead, our active
learning approach aims to minimize the number of annotations per-image. Our
method is enriched with semi-supervised learning, where we use pseudo labels
generated with a teacher-student framework to identify image regions that help
disambiguate confused classes. We also integrate mechanisms that enable better
performance on imbalanced label distributions, which have not been studied
previously for active learning in semantic segmentation. In experiments on the
CamVid and CityScapes datasets, our method obtains over 95% of the network's
performance on the full-training set using less than 17% of the training data,
whereas the previous state of the art required 40% of the training data.

提出了通过结合主动学习和半监督学习来减少标注样本数量的新算法，应用于语义分割任务取得了优异的成果。