Rhetorical Role Labeling (RRL) of legal judgments is essential for various
tasks, such as case summarization, semantic search and argument mining.
However, it presents challenges such as inferring sentence roles from context,
interrelated roles, limited annotated data, and label imbalance. This study
introduces novel techniques to enhance RRL performance by leveraging knowledge
from semantically similar instances (neighbours). We explore inference-based
and training-based approaches, achieving remarkable improvements in challenging
macro-F1 scores. For inference-based methods, we explore interpolation
techniques that bolster label predictions without re-training. While in
training-based methods, we integrate prototypical learning with our novel
discourse-aware contrastive method that work directly on embedding spaces.
Additionally, we assess the cross-domain applicability of our methods,
demonstrating their effectiveness in transferring knowledge across diverse
legal domains.

通过提出适用于法律判决的修辞角色标签（RRL）的新技术，利用来自语义上相似的实例（邻居）的知识，加强了 RRL 的性能，并通过推理和训练方法，结合 prototypical learning 和新颖的基于话语的对比方法，在嵌入空间上直接工作，实现了宏观 F1 得分的显著提升，同时评估了方法的跨领域适用性，展示了在不同法律领域之间传递知识的有效性。

留意你的邻居：利用类似实例进行法律文件的修辞角色标注

Mind Your Neighbours: Leveraging Analogous Instances for Rhetorical Role  Labeling for Legal Documents

A legal document is usually long and dense requiring human effort to parse
it. It also contains significant amounts of jargon which make deriving insights
from it using existing models a poor approach. This paper presents the
approaches undertaken to perform the task of rhetorical role labelling on
Indian Court Judgements as part of SemEval Task 6: understanding legal texts,
shared subtask A. We experiment with graph based approaches like Graph
Convolutional Networks and Label Propagation Algorithm, and transformer-based
approaches including variants of BERT to improve accuracy scores on text
classification of complex legal documents.

本文探讨了使用图卷积网络和标签传播算法等基于图的方法以及变种的 BERT 等变压器为基础的方法，在执行 SemEval 任务 6 的过程中进行了修辞角色标注任务，以理解印度法律文件并提高复杂法律文档的文本分类准确性得分。

使用 Transformer 和图神经网络对法律文件进行修辞角色标注

Rhetorical Role Labeling of Legal Documents using Transformers and Graph  Neural Networks

Segmentation and Rhetorical Role Labeling of legal judgements play a crucial
role in retrieval and adjacent tasks, including case summarization, semantic
search, argument mining etc. Previous approaches have formulated this task
either as independent classification or sequence labeling of sentences. In this
work, we reformulate the task at span level as identifying spans of multiple
consecutive sentences that share the same rhetorical role label to be assigned
via classification. We employ semi-Markov Conditional Random Fields (CRF) to
jointly learn span segmentation and span label assignment. We further explore
three data augmentation strategies to mitigate the data scarcity in the
specialized domain of law where individual documents tend to be very long and
annotation cost is high. Our experiments demonstrate improvement of span-level
prediction metrics with a semi-Markov CRF model over a CRF baseline. This
benefit is contingent on the presence of multi sentence spans in the document.

本文研究了在法律文件中识别多句子共享同一修辞角色标签的跨度分割问题，使用了半马尔可夫条件随机场模型进行标签分类，辅助三种数据增强策略，通过实验证明了该方法在提升跨度级预测度量方面的优点，需要文件中具有多句子跨度。