标签增强型原型网络用于法律判断预测
Junyi Chen1, Yingjie Han1, Xiabing Zhou2
1School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China.
Entropy (Basel, Switzerland)
|October 28, 2023
概括
本研究引入了一种新的标签增强型原型网络 (LPN),以解决法律判断预测 (LJP) 的法律人工智能 (AI) 的不平衡数据. 通过更好地处理各种法律案例标签,LPN方法提高了预测准确性.
科学领域:
- 法律人工智能 法律人工智能
- 机器学习 机器学习
- 计算法 计算法 计算法
背景情况:
- 法律判断预测 (LJP) 在法律AI中至关重要,特别是在民法系统中.
- 现有的LJP模型在与不平衡的标签分布作斗争,导致偏向高频标签.
- 这种不平衡阻碍了对不常见但重要的法律结果的准确预测.
研究的目的:
- 提出一种新的方法,即标签增强型原型网络 (LPN),以应对LJP中标签分配不平衡的挑战.
- 通过结合标签特定特征来提高法律判断预测模型的性能.
- 提高AI在法律决策中的公平性和准确性.
主要方法:
- 开发了使用统一编码和单独解码策略的标签增强原型网络 (LPN).
- 利用多尺度卷积神经网络编码案例事实描述以捕捉远距离特征.
- 整合了一个具有标签语义特征的原型网络和原型-原型损失,以优化表示学习.
主要成果:
- 拟议的LPN方法在两个现实数据集上显示了LJP性能的显著改善.
- 在两个子任务中,与最先进的模型相比,F1平均得分提高了1.23%和1.13%.
- 该方法有效地减轻因法律数据中的标签分配不平衡而引起的偏见.
结论:
- 标签增强型原型网络 (LPN) 提供了一个强大的解决方案,用于用不平衡的数据集进行法律判断预测.
- 这种方法提高了人工智能的能力,可以准确地预测不同类型案件中的法律判决.
- 这些发现表明,开发更公平,更有效的法律人工智能系统是一个有希望的方向.
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