Automatic Grammar Correction of Commas in Czech Written Texts : Comparative Study
| Authors | |
|---|---|
| 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 | |
| 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. |
| Related projects: |