  Dialogue systems are increasingly using knowledge bases (KBs) storing
real-world facts to help generate quality responses. However, as the KBs are
inherently incomplete and remain fixed during conversation, it limits dialogue
systems' ability to answer questions and to handle questions involving entities
or relations that are not in the KB. In this paper, we make an attempt to
propose an engine for Continuous and Interactive Learning of Knowledge (CILK)
for dialogue systems to give them the ability to continuously and interactively
learn and infer new knowledge during conversations. With more knowledge
accumulated over time, they will be able to learn better and answer more
questions. Our empirical evaluation shows that CILK is promising.
