  I train models for the task of neural machine translation for
English-Hungarian and Hungarian-English, using the Hunglish2 corpus. The main
contribution of this work is evaluating different data augmentation methods
during the training of NMT models. I propose 5 different augmentation methods
that are structure-aware, meaning that instead of randomly selecting words for
blanking or replacement, the dependency tree of sentences is used as a basis
for augmentation. I start my thesis with a detailed literature review on neural
networks, sequential modeling, neural machine translation, dependency parsing
and data augmentation. After a detailed exploratory data analysis and
preprocessing of the Hunglish2 corpus, I perform experiments with the proposed
data augmentation techniques. The best model for Hungarian-English achieves a
BLEU score of 33.9, while the best model for English-Hungarian achieves a BLEU
score of 28.6.
