Earticle

초록

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.

목차

1. 서론
 2. 인공지능 신경망 (Artificial Neural Network)
  2.1. 인공신경망의 구조
  2.2. 인공신경망의 학습방법
  2.3. 인공신경망과 특징표현
 3. 언어의 분산표현
  3.1. 단어의 분산표현
  3.2. 단어 분산표현의 확장
  3.3. 순환 신경망(Recurrent Neural Network; RNN)
 4. 신경망 방식의 기계번역
  4.1. 부호기-복호기 모델 (Encoder-Decoder Model)
  4.2. 주의 기구 (attention mechanism) 도입
  4.3. 부호기의 확장
 5. 기계번역의 역변환 시험
  5.1. 화제(topic) 문장 번역
  5.2 내포문 번역
  5.3. 이/가가 목적격조사로 사용된 구문
 6. 결론
 인용문헌
 [Abstract]

저자

• 지인영 [ In-Young Jhee | 한국체육대학교 ] 제1저자
• 김희동 [ Hee-Dong Kim | 한국외국어대학교 ] 제2저자

참고문헌