Adeline Müller


2022

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Linguistic Corpus Annotation for Automatic Text Simplification Evaluation
Rémi Cardon | Adrien Bibal | Rodrigo Wilkens | David Alfter | Magali Norré | Adeline Müller | Watrin Patrick | Thomas François
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Evaluating automatic text simplification (ATS) systems is a difficult task that is either performed by automatic metrics or user-based evaluations. However, from a linguistic point-of-view, it is not always clear on what bases these evaluations operate. In this paper, we propose annotations of the ASSET corpus that can be used to shed more light on ATS evaluation. In addition to contributing with this resource, we show how it can be used to analyze SARI’s behavior and to re-evaluate existing ATS systems. We present our insights as a step to improve ATS evaluation protocols in the future.

2020

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AMesure: A Web Platform to Assist the Clear Writing of Administrative Texts
Thomas François | Adeline Müller | Eva Rolin | Magali Norré
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: System Demonstrations

This article presents the AMesure platform, which aims to assist writers of French administrative texts in simplifying their writing. This platform includes a readability formula specialized for administrative texts and it also uses various natural language processing (NLP) tools to analyze texts and highlight a number of linguistic phenomena considered difficult to read. Finally, based on the difficulties identified, it offers pieces of advice coming from official plain language guides to users. This paper describes the different components of the system and reports an evaluation of these components.

2016

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Classification automatique de dictées selon leur niveau de difficulté de compréhension et orthographique (Automatic classification of dictations according to their complexity for comprehension and writing production)
Adeline Müller | Thomas Francois | Sophie Roekhaut | Cedrick Fairon
Actes de la conférence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Posters)

Cet article présente une approche visant à évaluer automatiquement la difficulté de dictées en vue de les intégrer dans une plateforme d’apprentissage de l’orthographe. La particularité de l’exercice de la dictée est de devoir percevoir du code oral et de le retranscrire via le code écrit. Nous envisageons ce double niveau de difficulté à l’aide de 375 variables mesurant la difficulté de compréhension d’un texte ainsi que les phénomènes orthographiques et grammaticaux complexes qu’il contient. Un sous-ensemble optimal de ces variables est combiné à l’aide d’un modèle par machines à vecteurs de support (SVM) qui classe correctement 56% des textes. Les variables lexicales basées sur la liste orthographique de Catach (1984) se révèlent les plus informatives pour le modèle.