Earticle

초록

영어

In this study, we discuss the basic technology of dialogue systems based on the deep neural network for natural language processing(NLP). Deep learning has become a basic technique in dialogue systems. Many researchers investigated on applying neural networks to the different components of a traditional task-oriented dialogue system, including natural language understanding, dialogue state tracking, and natural language generation, We study the recent technical advances on task-oriented dialogue systems and extent to the non-task-oriented system. We find that the end-to-end models are prevailing and representative in most of recent research papers. Also, it is blurring the boundaries between the task-oriented dialogue systems and non-task-oriented systems. In particular, the chit-chat dialogues of generative method are modeled by the end-to-end model directly. The task-oriented systems are also moving towards an end-to-end model with reinforcement learning. It is noting that current end-to-end models are still improving. We discuss some possible research directions and the technical challenges. In the future, we expect that accuracy of dialogue system will be improved further by employing reinforcement learning method.

목차

1. 서론
2. 대화시스템의 종류와 구성
2.1. 대화시스템의 종류
2.2 대화시스템의 구성
3. 신경망 방식의 자연어처리 기술
3.1 순환 신경망과 콘볼루션 신경망
3.2. 부호기-복호기 모델 (Encoder-Decoder Model)
3.3 강화학습 (Reinforcement Learning) 방식
3.4 생성대립네트워크 (Generative Adversarial Networks: GAN)
4. 대화시스템의 구현 기술 분석
4.1 언어이해 모듈
4.2 대화관리모듈
4.3 언어생성모듈
4.4 단대단 방식 목적대화시스템 분석
4.5 대화지향시스템 기술분석
4.6 대화시스템 기술적 도전과제
5. 결론
인용문헌
[Abstract]

저자

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

참고문헌