提出新评估方法JUDGEBERT，精准衡量法律文本简化中的语义保留度。
JUDGEBERT: Assessing Legal Meaning Preservation Between Sentences
- 基于法国法律文本设计新评估指标JUDGEBERT，融合语义一致性判断
- 与人工判断相关性更高，且在相同/无关句对上分别得分为100%与0%
- 适用于法律文本简化场景，助力专业人士与公众理解法律条文
在法律等敏感领域，简化文本同时保持其原意是一项复杂而关键的任务。本文提出FrJUDGE数据集，用于评估两段法律文本间的语义保留程度，并引入JUDGEBERT这一新型评估指标，专门针对法语文本简化任务。JUDGEBERT在与人工判断的相关性上优于现有指标，且通过两项关键合理性检验：对于完全相同的句子始终输出100%得分；对于完全无关的句子则始终输出0%。研究结果表明，该指标具备显著潜力，可提升法律自然语言处理应用的准确性与可访问性，服务于法律从业者及普通用户。
原文摘要 · Abstract (English)
Simplifying text while preserving its meaning is a complex yet essential task, especially in sensitive domain applications like legal texts. When applied to a specialized field, like the legal domain, preservation differs significantly from its role in regular texts. This paper introduces FrJUDGE, a new dataset to assess legal meaning preservation between two legal texts. It also introduces JUDGEBERT, a novel evaluation metric designed to assess legal meaning preservation in French legal text simplification. JUDGEBERT demonstrates a superior correlation with human judgment compared to existing metrics. It also passes two crucial sanity checks, while other metrics did not: For two identical sentences, it always returns a score of 100%; on the other hand, it returns 0% for two unrelated sentences. Our findings highlight its potential to transform legal NLP applications, ensuring accuracy and accessibility for text simplification for legal practitioners and lay users.
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