Tättar, Andre, juhendajaTars, MaaliTartu Ülikool. Loodus- ja täppisteaduste valdkondTartu Ülikool. Arvutiteaduse instituut2023-10-262023-10-262023https://hdl.handle.net/10062/93787First high-quality machine translation models were mainly focusing on large languages, such as English and German. Thankfully, the trend has been growing toward helping languages with fewer resources. Most Finno-Ugric languages are low-resource and require the help of different techniques and larger languages for additional information during translation. Recently, multiple big companies have released multilingual pre-trained neural machine translation models that can be adapted to low-resource languages. However, some of the Finno-Ugric languages included in our work were not included in the training of these pre-trained models. Thus, we need to use cross-lingual transfer for fine-tuning the models to our selected languages. In addition, we do data augmentation by back-translation to alleviate the data scarcity issue of low-resource languages. We train multiple different models to determine the best setting for our selected languages and improve over previous results for all language pairs. As a result, we deploy the best model and create the first multilingual NMT system for multiple low-resource Finno-Ugric languages.engopenAccessAttribution-NonCommercial-NoDerivatives 4.0 Internationalneural networksautomatic learningmachine translationlanguage technologytransfer learningmagistritöödinformaatikainfotehnoloogiainformaticsinfotechnologyThesis