Identification and Correction of Grammatical Errors in Ukrainian Texts Based on Machine Learning Technology
Autor(en): | Lytvyn, Vasyl Pukach, Petro Vysotska, Victoria Vovk, Myroslava Kholodna, Nataliia |
Stichwörter: | CHECKER; deep learning; error correction; grammatical error correction; LANGUAGE; machine learning; Mathematics; neural network; NLP; text analysis; text classification; text pre-processing | Erscheinungsdatum: | 2023 | Herausgeber: | MDPI | Journal: | MATHEMATICS | Volumen: | 11 | Ausgabe: | 4 | Zusammenfassung: | 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. |
DOI: | 10.3390/math11040904 |
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geprüft am 23.05.2024