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A Study on Conversational AI Agent based on Continual Learning

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2023, 28(1), pp.27-38
  • DOI : 10.9708/jksci.2023.28.01.027
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : December 7, 2022
  • Accepted : December 30, 2022
  • Published : January 31, 2023

Chae-Lim Park 1 So-Yeop Yoo 1 Ok-Ran Jeong 1

1가천대학교

Accredited

ABSTRACT

In this paper, we propose a conversational AI agent based on continual learning that can continuously learn and grow with new data over time. A continual learning-based conversational AI agent consists of three main components: Task manager, User attribute extraction, and Auto-growing knowledge graph. When a task manager finds new data during a conversation with a user, it creates a new task with previously learned knowledge. The user attribute extraction model extracts the user’s characteristics from the new task, and the auto-growing knowledge graph continuously learns the new external knowledge. Unlike the existing conversational AI agents that learned based on a limited dataset, our proposed method enables conversations based on continuous user attribute learning and knowledge learning. A conversational AI agent with continual learning technology can respond personally as conversations with users accumulate. And it can respond to new knowledge continuously. This paper validate the possibility of our proposed method through experiments on performance changes in dialogue generation models over time.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.