오픈소스 챗봇빌더를 사용한 챗봇 서비스 아키텍처 모델 연구 = A study on a Chatbot Service Model Architecture using Open Source Chatbot Builders

다국어 초록 (Multilingual Abstract)

Due to the development of IT technology and the on-going Coronavirus disease, non-face-to-face services have been activated. To overcome the inconvenience of non-face-to-face service, service providers have adopted chatbots as a way to feel like a human being. As the increasing chatbot services, chatbot builders have emerged, which can help non-developers to build them. Although its popularity has increased, its performance evaluation has not been conducted on such chatbot builders. In this paper, we implement a prototype chatbot that classifies hospital departments in the medical field using Dialogflow and Rasa, which are popular chatbot builders. By measuring the accuracy of the chatbot's classification of medical subjects, we evaluated the level of accuracy that the most used chatbot builder can have when they are used to build a chatbot service. The simulation results showed that Dialogflow had 87%, 65%, and 60%, and Rasa did 64%, 70%, and 63% in surgery dermatology, and otolaryngology, respectively.

참고문헌 (Reference)

1 윤여범, "인공지능 챗봇을 활용한 초등영어 말하기 지도의 전망: Dialogflow를 중심으로" 초등교육연구원 32 : 15-28, 2021

2 정혜경 ; 나정조, "인공지능 기반 챗봇 서비스를 활용한 와인 추천 앱개발" 한국반도체디스플레이기술학회 18 (18): 93-99, 2019

3 T. Bocklisch, "Rasa: Open Source Language Understanding and Dialogue Management" 2017

4 T. Lalwani, "Implementation of a Chatbot System using AI and NLP" 6 : 2018

5 J. Weizenbaum, "ELIZA A Computer Program For the Study of Natural Language Communication Between Man and Machine" 9 : 36-45, 1966

6 A. F. Muhammad, "Developing English Conversation Chatbot Using Dialogflow" 2020

7 S. Salvi, "A Conversational Smart Home Assistant Built on Telegram and Google Dialogflow" 2019

8 A. Abdellatif, "A Comparison of Natural Language Understanding Platforms for Chatbots in Software Engineering" 1-1, 2021