JUDGEBERT: Assessing Legal Meaning Preservation Between Sentences
Bibliographic record
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.Annotators.We selected five native Frenchspeaking law students at the Faculty of Law of University Laval as our annotators.A meeting was held with them to introduce the task, instructions, and annotation guide and interface.Instructions included that they must spend at most 5 minutes per sentence pair.Furthermore, 15 instances were annotated during a pilot phase to familiarize them with the task.Finally, during a second meeting after evaluating the practice instance, annotators received feedback and advice on what phenomena
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".