Proficiency Level Classification of Foreign Language Learners Using Machine Learning Algorithms and Multilingual Models

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3AZZ84PN8N" target="_blank" >RIV/00216208:11320/22:ZZ84PN8N - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/978-3-031-16014-1_21" target="_blank" >https://doi.org/10.1007/978-3-031-16014-1_21</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-16014-1_21" target="_blank" >10.1007/978-3-031-16014-1_21</a>

Result language

angličtina

Original language name

Proficiency Level Classification of Foreign Language Learners Using Machine Learning Algorithms and Multilingual Models

Original language description

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 – which served as benchmarks – 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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

—

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Computational Collective Intelligence

ISBN

978-3-031-16014-1

ISSN

—

e-ISSN

—

Number of pages

11

Pages from-to

261-271

Publisher name

Springer International Publishing

Place of publication

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Event location

Cham

Event date

Jan 1, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000871920200021