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This work was conducted to find out how tokenization methods affect thetraining results of machine translation models. In this work, alphabettokenization, morpheme tokenization, and BPE tokenization were applied toKorean as the source language and English as the target language respectively,and the comparison experiment was conducted by repeating 50,000 epochs of each9 models using the Transformer neural network. As a result of measuring theBLEU scores of the experimental models, the model that applied BPE tokenizationto Korean and morpheme tokenization to English recorded 35.73, showing the bestperformance.

Korean-English Machine Translation with Multiple Tokenization Strategy