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    <name type="personal">
        <namePart type="given">Pierre-Luc</namePart>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T JUDGEBERT: Assessing Legal Meaning Preservation Between Sentences
%A Beauchemin, David
%A Albert-Rochette, Michelle
%A Khoury, Richard
%A Déziel, Pierre-Luc
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F beauchemin-etal-2025-judgebert
%X 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.
%U https://aclanthology.org/2025.emnlp-main.5/
%P 92-118

Markdown (Informal)

[JUDGEBERT: Assessing Legal Meaning Preservation Between Sentences](https://aclanthology.org/2025.emnlp-main.5/) (Beauchemin et al., EMNLP 2025)

• JUDGEBERT: Assessing Legal Meaning Preservation Between Sentences (Beauchemin et al., EMNLP 2025)

ACL

• David Beauchemin, Michelle Albert-Rochette, Richard Khoury, and Pierre-Luc Déziel. 2025. JUDGEBERT: Assessing Legal Meaning Preservation Between Sentences. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 92–118, Suzhou, China. Association for Computational Linguistics.