Lifelong Knowledge Learning in Rule-based Dialogue Systems
This work addresses the problem of chatbots not learning online from user interactions, which is a significant limitation for real-life deployed rule-based chatbots.
This paper proposes a method for rule-based chatbots to continuously acquire new knowledge from users during conversations. The goal is to expand the chatbot's knowledge base and improve its service by enabling online learning after deployment.
One of the main weaknesses of current chatbots or dialogue systems is that they do not learn online during conversations after they are deployed. This is a major loss of opportunity. Clearly, each human user has a great deal of knowledge about the world that may be useful to others. If a chatbot can learn from their users during chatting, it will greatly expand its knowledge base and serve its users better. This paper proposes to build such a learning capability in a rule-based chatbot so that it can continuously acquire new knowledge in its chatting with users. This work is useful because many real-life deployed chatbots are rule-based.