  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.
