  This ability to learn consecutive tasks without forgetting how to perform
previously trained problems is essential for developing an online dialogue
system. This paper proposes an effective continual learning for the
task-oriented dialogue system with iterative network pruning, expanding and
masking (TPEM), which preserves performance on previously encountered tasks
while accelerating learning progress on subsequent tasks. Specifically, TPEM
(i) leverages network pruning to keep the knowledge for old tasks, (ii) adopts
network expanding to create free weights for new tasks, and (iii) introduces
task-specific network masking to alleviate the negative impact of fixed weights
of old tasks on new tasks. We conduct extensive experiments on seven different
tasks from three benchmark datasets and show empirically that TPEM leads to
significantly improved results over the strong competitors. For
reproducibility, we submit the code and data at:
https://github.com/siat-nlp/TPEM
