Experiments with Universal CEFR Classification
This addresses the challenge of developing consistent language proficiency assessment tools for learners across different languages, though it appears incremental as it builds on existing classification approaches.
The paper tackled the problem of universal automated proficiency classification across languages using the CEFR scale, finding that monolingual and multilingual models achieved similar performance, while cross-lingual classification yielded lower but comparable results.
The Common European Framework of Reference (CEFR) guidelines describe language proficiency of learners on a scale of 6 levels. While the description of CEFR guidelines is generic across languages, the development of automated proficiency classification systems for different languages follow different approaches. In this paper, we explore universal CEFR classification using domain-specific and domain-agnostic, theory-guided as well as data-driven features. We report the results of our preliminary experiments in monolingual, cross-lingual, and multilingual classification with three languages: German, Czech, and Italian. Our results show that both monolingual and multilingual models achieve similar performance, and cross-lingual classification yields lower, but comparable results to monolingual classification.