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

Continual Learning for Task-oriented Dialogue System with Iterative Network Pruning, Expanding and Masking