Proficiency Level Classification of Foreign Language Learners Using Machine Learning Algorithms and Multilingual Models
Abstract
This paper addresses the problem of classifying the proficiency of second language learners using multilingual models. Such models can be extremely useful in applications supporting the learning of multiple, even rare languages.
Experiments based on Czech, German and Italian languages have been reported in the literature. This dataset was extended with texts in English.
SVM, random forest, and logistic regression methods were used to train the model with different sets of language features. For the monolingual models â which served as benchmarks â the best results were observed for the random forest and SVM methods.
For multilingual models, in contrast to other studies, the best results were obtained using the SVM algorithm. Models trained on a feature set containing n-grams of POS, n-grams of dependencies, and POS distribution performed better than models trained only on n-grams of POS, used in other works on multilingual models.
The experiments confirmed the feasibility of using multilingual models in place of monolingual ones. Multilingual models were also able to classify texts in a language that was not involved in model learning.