earticle

영어

In this study, we discuss the basic technology of machine learning of the deep neural network for natural language processing(NLP). We explain the distributed vector representation of words. Distributed vector representation is proved to be able to carry semantic meanings and are useful in various NLP tasks. The recurrent neural network(RNN) is employed to get the vector representation of sentences. We discuss the RNN encoder-decoder model and some modifications of the RNN structure to improve the accuracy of the machine translations. To test and verify the accuracy of Google translator, we performed the translation among Korean, English, and Japanese, and examined the meaning change between the original and the translated sentence. In neural network translation, we showed some inaccuracies of the translation such as wrong relation between subject and object, or some omission or repetition of the original meaning. In order to increase the performance and accuracy of machine translation, it is necessary to acquire more data for training.