  Slot filling and intent detection have become a significant theme in the
field of natural language understanding. Even though slot filling is
intensively associated with intent detection, the characteristics of the
information required for both tasks are different while most of those
approaches may not fully aware of this problem. In addition, balancing the
accuracy of two tasks effectively is an inevitable problem for the joint
learning model. In this paper, a Continual Learning Interrelated Model (CLIM)
is proposed to consider semantic information with different characteristics and
balance the accuracy between intent detection and slot filling effectively. The
experimental results show that CLIM achieves state-of-the-art performace on
slot filling and intent detection on ATIS and Snips.
