Strategic Adaptations for Better Reading and Text Comprehension in FFL

The heterogeneity of language learners' levels is very common within the same class, and managing this diversity represents a major challenge for language teachers, who should provide personalized resources to each learner. Thus, the STAR-FLE project aims to propose innovative digital solutions in the area of Natural Language Processing (NLP) that may improve text comprehension for French L2 learners and assist teachers in managing multiple levels of learners. 

We propose context-based aids for understanding lexical issues as well as MWE (multi-word expressions) found in authentic texts. Our system provides MWE identification, generation of definitions tailored to specific learner profiles, synonym search, word sense disambiguation, along with the option to choose simpler synonyms for better comprehension of texts. 

On the other hand, we are developing original NLP resources such as an annotated CEFR corpus and lexicons, as well as an MWE-annotated corpus.