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.
@article{arxiv.2011.09811,
title = {Lifelong Knowledge Learning in Rule-based Dialogue Systems},
author = {Bing Liu and Chuhe Mei},
journal= {arXiv preprint arXiv:2011.09811},
year = {2020}
}