Fišel, Mark, juhendajaLuhtaru, AgnesTartu Ülikool. Loodus- ja täppisteaduste valdkondTartu Ülikool. Arvutiteaduse instituut2023-10-272023-10-272020https://hdl.handle.net/10062/93808We introduce an approach to grammatical error correction that does not require annotated training data. We train a multilingual neural machine translation model that uses only language-parallel translations. There are more openly available translations available than grammatical error correction corpora, especially for low-resource languages like Estonian. We find out that this system has high recall but low precision. So it corrects plenty of mistakes but adds many mistakes to correct text. Adding artificial mistakes increases the recall and has really positive impact on spelling error correction. Our model reliably corrects grammatical errors, like subject-verb agreement and noun number, but struggles with lexical errors and unnecessary paraphrasing.estopenAccessAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/natural language processingneural machine translationgrammatical error correctionbakalaureusetöödinformaatikainfotehnoloogiainformaticsinfotechnologyThesis