Lifelong Knowledge Learning in Rule-based Dialogue Systems
- URL: http://arxiv.org/abs/2011.09811v1
- Date: Thu, 19 Nov 2020 13:33:12 GMT
- Title: Lifelong Knowledge Learning in Rule-based Dialogue Systems
- Authors: Bing Liu and Chuhe Mei
- Abstract summary: This paper proposes to build such a learning capability in a rule-based chatbots 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.
- Score: 10.229787631112742
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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