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

Word embedding is a modern distributed word representations approach widely
used in many natural language processing tasks. Converting the vocabulary in a
legal document into a word embedding model facilitates subjecting legal
documents to machine learning, deep learning, and other algorithms and
subsequently performing the downstream tasks of natural language processing
vis-à-vis, for instance, document classification, contract review, and
machine translation. The most common and practical approach of accuracy
evaluation with the word embedding model uses a benchmark set with linguistic
rules or the relationship between words to perform analogy reasoning via
algebraic calculation. This paper proposes establishing a 1,134 Legal
Analogical Reasoning Questions Set (LARQS) from the 2,388 Chinese Codex corpus
using five kinds of legal relations, which are then used to evaluate the
accuracy of the Chinese word embedding model. Moreover, we discovered that
legal relations might be ubiquitous in the word embedding model.

本研究提出了使用五种法律关系从 2,388 个中国法典语料库中建立一个 1,134 个法律类比推理问题集 (LARQS)， 以评估中文词嵌入模型的准确性，并发现法律关系可能是词嵌入模型中普遍存在的。