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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

—

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

Computational Collective Intelligence

ISBN

978-3-031-16014-1

ISSN

—

e-ISSN

—

Počet stran výsledku

11

Strana od-do

261-271

Název nakladatele

Springer International Publishing

Místo vydání

—

Místo konání akce

Cham

Datum konání akce

1. 1. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000871920200021