Earticle

초록

영어

Machine translation is one of the classic sub-fields of natural language processing, a field that has been studied for a long time. There are several methods related to machine translation, and neural machine translation is one of them. Neural machine translation is a method that translates the source sentence to the target sentence through a neural network model. Recently, the transformer model using the attention mechanism has become the SOTA technique in the field. In this research, the transformer model using the attention mechanism is trained through 2 parallel corpora: English-German and English-Korean. The results of translations are evaluated with a BLEU score for each. The transformer shows an 8.60 BLEU score when trained by the English-German parallel corpora, and a 0.43 BLEU score in the case of English-Korean. It seems like the reason why the performance of English-German translations is higher than the performance of English-Korean translations is that English and Germany are in the Germanic language family, which is the same. In contrast, unlike English, Korean belongs to Altaic or Isolated languages and has different linguistic characteristics. The performance of the transformer is also affected by the quality of the parallel corpus used for training. It can be interpreted that it is necessary to secure a higher quality English-Korean parallel corpus.

목차

Abstract
I. INTRODUCTION
II. BACKGROUNDS
A. Rule-based machine translation
B. Statistical machine translation
C. Neural machine translation
III. METHOD
A. Dataset
B. Experiment Environment
IV. RESULT
V. DISCUSSION
VI. CONCLUSION AND FUTURE WORK
REFERENCES

저자

• Jongho Won [ Department of Electrical and Computer Engineering Inha University ]
• Min Dong Jin [ Department of Electrical and Computer Engineering Inha University ]
• Deok-Hwan Kim [ Department of Electrical and Computer Engineering Inha University ] Corresponding Author

참고문헌