  Development of language proficiency models for non-native learners has been
an active area of interest in NLP research for the past few years. Although
language proficiency is multidimensional in nature, existing research typically
considers a single "overall proficiency" while building models. Further,
existing approaches also considers only one language at a time. This paper
describes our experiments and observations about the role of pre-trained and
fine-tuned multilingual embeddings in performing multi-dimensional,
multilingual language proficiency classification. We report experiments with
three languages -- German, Italian, and Czech -- and model seven dimensions of
proficiency ranging from vocabulary control to sociolinguistic appropriateness.
Our results indicate that while fine-tuned embeddings are useful for
multilingual proficiency modeling, none of the features achieve consistently
best performance for all dimensions of language proficiency. All code, data and
related supplementary material can be found at:
https://github.com/nishkalavallabhi/MultidimCEFRScoring.
