Predicting legal judgments with reliable confidence is paramount for
responsible legal AI applications. While transformer-based deep neural networks
(DNNs) like BERT have demonstrated promise in legal tasks, accurately assessing
their prediction confidence remains crucial. We present a novel Bayesian
approach called BayesJudge that harnesses the synergy between deep learning and
deep Gaussian Processes to quantify uncertainty through Bayesian kernel Monte
Carlo dropout. Our method leverages informative priors and flexible data
modelling via kernels, surpassing existing methods in both predictive accuracy
and confidence estimation as indicated through brier score. Extensive
evaluations of public legal datasets showcase our model's superior performance
across diverse tasks. We also introduce an optimal solution to automate the
scrutiny of unreliable predictions, resulting in a significant increase in the
accuracy of the model's predictions by up to 27\%. By empowering judges and
legal professionals with more reliable information, our work paves the way for
trustworthy and transparent legal AI applications that facilitate informed
decisions grounded in both knowledge and quantified uncertainty.

利用贝叶斯方法和深度高斯过程，我们提出了一种名为 BayesJudge 的新型贝叶斯方法，通过贝叶斯千次采样 dropout 量化预测的不确定性，提高了预测的准确性和置信度评估，从而实现可靠的法律 AI 应用。

BayesJudge：基于贝叶斯核语言建模的法院判决预测中的置信度不确定性

BayesJudge: Bayesian Kernel Language Modelling with Confidence  Uncertainty in Legal Judgment Prediction

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

Analysis and extraction of useful information from legal judgments using
computational linguistics was one of the earliest problems posed in the domain
of information retrieval. Presently, several commercial vendors exist who
automate such tasks. However, a crucial bottleneck arises in the form of
exorbitant pricing and lack of resources available in analysis of judgements
mete out by Hong Kong's Legal System. This paper attempts to bridge this gap by
providing several statistical, machine learning, deep learning and zero-shot
learning based methods to effectively analyze legal judgments from Hong Kong's
Court System. The methods proposed consists of: (1) Citation Network Graph
Generation, (2) PageRank Algorithm, (3) Keyword Analysis and Summarization, (4)
Sentiment Polarity, and (5) Paragrah Classification, in order to be able to
extract key insights from individual as well a group of judgments together.
This would make the overall analysis of judgments in Hong Kong less tedious and
more automated in order to extract insights quickly using fast inferencing. We
also provide an analysis of our results by benchmarking our results using Large
Language Models making robust use of the HuggingFace ecosystem.

本文探讨了使用统计分析、机器学习、深度学习、零样本学习等基于自然语言处理技术的方法，以有效地分析来自香港法院系统的法律判决，并提供了五种方法，从而能够从个人以及一组判决中提取关键见解和极性 sentiment，以便快速提取洞见并使香港判决的整体分析更少烦琐、更自动化。