Identification and Correction of Grammatical Errors in Ukrainian Texts Based on Machine Learning Technology

Vasyl Lytvyn, Petro Pukach, Victoria Vysotska, Myroslava Vovk () and Nataliia Kholodna
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Vasyl Lytvyn: Information Systems and Networks Department, Lviv Polytechnic National University, 12 Bandera Str., 79013 Lviv, Ukraine
Petro Pukach: Institute of Applied Mathematics and Fundamental Sciences, Lviv Polytechnic National University, 12 Bandera Str., 79013 Lviv, Ukraine
Victoria Vysotska: Information Systems and Networks Department, Lviv Polytechnic National University, 12 Bandera Str., 79013 Lviv, Ukraine
Myroslava Vovk: Institute of Applied Mathematics and Fundamental Sciences, Lviv Polytechnic National University, 12 Bandera Str., 79013 Lviv, Ukraine
Nataliia Kholodna: Information Systems and Networks Department, Lviv Polytechnic National University, 12 Bandera Str., 79013 Lviv, Ukraine

Mathematics, 2023, vol. 11, issue 4, 1-19

Abstract: A machine learning model for correcting errors in Ukrainian texts has been developed. It was established that the neural network has the ability to correct simple sentences written in Ukrainian; however, the development of a full-fledged system requires the use of spell-checking using dictionaries and the checking of rules, both simple and those based on the result of parsing dependencies or other features. In order to save computing resources, a pre-trained BERT (Bidirectional Encoder Representations from Transformer) type neural network was used. Such neural networks have half as many parameters as other pre-trained models and show satisfactory results in correcting grammatical and stylistic errors. Among the ready-made neural network models, the pre-trained neural network model mT5 (a multilingual variant of T5 or Text-to-Text Transfer Transformer) showed the best performance according to the BLEU (bilingual evaluation understudy) and METEOR (metric for evaluation of translation with explicit ordering) metrics.

Keywords: NLP; text pre-processing; error correction; grammatical error correction; machine learning; deep learning; text analysis; text classification; neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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