  This work was conducted to find out how tokenization methods affect the
training results of machine translation models. In this work, alphabet
tokenization, morpheme tokenization, and BPE tokenization were applied to
Korean as the source language and English as the target language respectively,
and the comparison experiment was conducted by repeating 50,000 epochs of each
9 models using the Transformer neural network. As a result of measuring the
BLEU scores of the experimental models, the model that applied BPE tokenization
to Korean and morpheme tokenization to English recorded 35.73, showing the best
performance.
