Low-resource Finno-Ugric Neural Machine Translation through Cross-lingual Transfer Learning
Name
Maali Tars
Abstract
First 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.
Graduation Thesis language
English
Graduation Thesis type
Master - Computer Science
Supervisor(s)
Andre Tättar
Defence year
2023