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

In this paper, we introduce a new French Data-to-Text (D2T) dataset in the legal domain: Plum2Text1. It is made out of plumitifs (docket files) - descriptions pairs that are derived from publicly available documents issued by Canadian criminal courts. The development of Plum2Text is primarily intended to train statistical natural language generation algorithms, in order to make the plumitifs more easily understandable for Canadian citizens. The inputs and outputs of the dataset are unique: on the data side, the values of the table contain long pieces of textual utterance, and on the text side (or reference), it most often consists of a paraphrase of the table values. We describe how we curated the plumitif-description associations by introducing an annotation tool and a methodology specific to the D2T natural language generation task. We do so by using simple yet efficient text classifiers to help the annotator leverage annotated examples during the annotation process. As a matter of privacy, we also illustrate how we are decontextualizing the descriptions.

Learning language representations is a key component in many natural language processing tasks, and their usefulness is most often challenged by specialized target domains and vocabulary. We have witnessed several neural causal language models (CLM) that learn contextual representations such as ELMo [8]. More recently, the Transformer architecture [10] has tremendously improved language representation learning, giving birth to new architectures such as BERT [4], a masked language model, pushing the state-of-the-art of natural language understanding to an unprecedented level of performance on standard benchmarks. Moreover, it has been found that Transformer-based CLM, such as GPT [9], are excellent feature extractors as well as being impressive text generators. BART [7], an architecture combining the backbone of both BERT and GTP proved to be particularly effective at generating text while being competitive in comprehension tasks. BARThez, the French version of BART, was recently introduced as a pre-trained model on a very large monolingual French corpus [6]. In this paper, we introduce CriminelBART, a fine-tuned version of BARThez specialized for criminal law using a French Canadian corpus of legal judgments, and we evaluate its performance on different tasks.

Plumitifs (dockets) were initially a tool for law clerks.Nowadays, they are used as summaries presenting all the steps of a judicial case.Information concerning parties' identity, jurisdiction in charge of administering the case, and some information relating to the nature and the course of the preceding are available through plumitifs.They are publicly accessible but barely understandable; they are written using abbreviations and referring to provisions from the Criminal Code of Canada, which makes them hard to reason about.In this paper, we propose a simple yet efficient multi-source language generation architecture that leverages both the plumitif and the Criminal Code's content to generate intelligible plumitifs descriptions.It goes without saying that ethical considerations rise with these sensitive documents made readable and available at scale, legitimate concerns that we address in this paper.This is, to the best of our knowledge, the first application of plumitifs descriptions generation made available for French speakers along with an ethical discussion about the topic.