Automatic Grammar Correction of Commas in Czech Written Texts: Comparative Study

Authors

MACHURA Jakub FRÉMUND Adam ŠVEC Jan

Year of publication 2022
Type Article in Proceedings
Conference Text, Speech, and Dialogue 25th International Conference, TSD 2022, Brno, Czech Republic, September 6–9, 2022, Proceedings
MU Faculty or unit

Faculty of Arts

Citation
Web Conference paper
Doi http://dx.doi.org/10.1007/978-3-031-16270-1_10
Keywords Grammatical error correction; Linguistic rules; Transfer learning
Description The task of grammatical error correction is a widely studied field of natural language processing where the traditional rule-based approaches compete with the machine learning methods. The rule-based approach benefits mainly from a wide knowledge base available for a given language. On the contrary, the transfer learning methods and especially the use of pre-trained Transformers have the ability to be trained from a huge number of texts in a given language. In this paper, we focus on the task of automatic correction of missing commas in Czech written texts and we compare the rule-based approach with the Transformer-based model trained for this task.
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