Eva Rolin


HECTOR: A Hybrid TExt SimplifiCation TOol for Raw Texts in French
Amalia Todirascu | Rodrigo Wilkens | Eva Rolin | Thomas François | Delphine Bernhard | Núria Gala
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Reducing the complexity of texts by applying an Automatic Text Simplification (ATS) system has been sparking interest inthe area of Natural Language Processing (NLP) for several years and a number of methods and evaluation campaigns haveemerged targeting lexical and syntactic transformations. In recent years, several studies exploit deep learning techniques basedon very large comparable corpora. Yet the lack of large amounts of corpora (original-simplified) for French has been hinderingthe development of an ATS tool for this language. In this paper, we present our system, which is based on a combination ofmethods relying on word embeddings for lexical simplification and rule-based strategies for syntax and discourse adaptations. We present an evaluation of the lexical, syntactic and discourse-level simplifications according to automatic and humanevaluations. We discuss the performances of our system at the lexical, syntactic, and discourse levels


FrenLyS: A Tool for the Automatic Simplification of French General Language Texts
Eva Rolin | Quentin Langlois | Patrick Watrin | Thomas François
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

Lexical simplification (LS) aims at replacing words considered complex in a sentence by simpler equivalents. In this paper, we present the first automatic LS service for French, FrenLys, which offers different techniques to generate, select and rank substitutes. The paper describes the different methods proposed by our tool, which includes both classical approaches (e.g. generation of candidates from lexical resources, frequency filter, etc.) and more innovative approaches such as the exploitation of CamemBERT, a model for French based on the RoBERTa architecture. To evaluate the different methods, a new evaluation dataset for French is introduced.


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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.