Automatic Text Simplification (ATS) aims at rewriting texts into simpler variants while preserving their original meaning, so they can be more easily understood by different audiences. While ATS has been widely used for written texts, its application to spoken language remains unexplored, even if it is not exempt from difficulty. This study aims to characterize the edit operations performed in order to simplify French transcripts for non-native speakers. To do so, we relied on a data sample randomly extracted from the Orféo-CEFC French spontaneous speech dataset. In the absence of guidelines to direct this process, we adopted an intuitive simplification approach, so as to investigate the crafted simplifications based on expert linguists’ criteria, and to compare them with those produced by a generative AI (namely, ChatGPT). The results, analyzed quantitatively and qualitatively, reveal that the most common edits are deletions, and affect oral production aspects, like restarts or hesitations. Consequently, candidate simplifications are typically register-standardized sentences that solely include the propositional content of the input. The study also examines the alignment between human- and machine-based simplifications, revealing a moderate level of agreement, and highlighting the subjective nature of the task. The findings contribute to understanding the intricacies of simplifying spontaneous spoken language. In addition, the provision of a small-scale parallel dataset derived from such expert simplifications, Propicto-Orféo-Simple, can facilitate the evaluation of speech simplification solutions.

We present the results of our constrained submission to the WMT 2024 shared task, which focuses on translating from Spanish into two low-resource languages of Spain: Aranese (spa-arn) and Aragonese (spa-arg). Our system integrates real and synthetic data generated by large language models (e.g., BLOOMZ) and rule-based Apertium translation systems. Built upon the pre-trained NLLB system, our translation model utilizes a multistage approach, progressively refining the initial model through the sequential use of different datasets, starting with large-scale synthetic or crawled data and advancing to smaller, high-quality parallel corpora. This approach resulted in BLEU scores of 30.1 for Spanish to Aranese and 61.9 for Spanish to Aragonese.

PROPICTO is a project funded by the French National Research Agency and the Swiss National Science Foundation, that aims at creating Speech-to-Pictograph translation systems, with a special focus on French as an input language. By developing such technologies, we intend to enhance communication access for non-French speaking patients and people with cognitive impairments.

The use of images has been shown to positively affect patient comprehension in medical settings, in particular to deliver specific medical instructions. However, tools that automatically translate sentences into pictographs are still scarce due to the lack of resources. Previous studies have focused on the translation of sentences into pictographs by using WordNet combined with rule-based approaches and deep learning methods. In this work, we showed how we leveraged the BabelDr system, a speech to speech translator for medical triage, to build a speech to pictograph translator using UMLS and neural machine translation approaches. We showed that the translation from French sentences to a UMLS gloss can be viewed as a machine translation task and that a Multilingual Neural Machine Translation system achieved the best results.

La Communication Alternative et Augmentée (CAA) prend une place importante chez les personnes en situation de handicap ainsi que leurs proches à cause de la difficulté de son utilisation. Pour réduire ce poids, l’utilisation d’outils de traduction de la parole en pictogrammes est pertinente. De plus, ils peuvent être d’une grande aide pour l’accessibilité communicative dans le milieu hospitalier. Dans cet article, nous présentons un projet de recherche visant à développer un système de traduction de la parole vers des pictogrammes. Il met en jeu une chaîne de traitement comportant plusieurs axes relevant du traitement automatique des langues et de la parole, tels que la reconnaissance automatique de la parole, l’analyse syntaxique, la simplification de texte et la traduction automatique vers les pictogrammes. Nous présentons les difficultés liées à chacun de ces axes ainsi que, pour certains, les pistes de résolution.