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Pre-trained models have achieved excellent performance on the dialogue task.However, for the continual increase of online chit-chat scenarios, directlyfine-tuning these models for each of the new tasks not only explodes thecapacity of the dialogue system on the embedded devices but also causesknowledge forgetting on pre-trained models and knowledge interference amongdiverse dialogue tasks. In this work, we propose a hierarchical inductivetransfer framework to learn and deploy the dialogue skills continually andefficiently. First, we introduce the adapter module into pre-trained models forlearning new dialogue tasks. As the only trainable module, it is beneficial forthe dialogue system on the embedded devices to acquire new dialogue skills withnegligible additional parameters. Then, for alleviating knowledge interferencebetween tasks yet benefiting the regularization between them, we further designhierarchical inductive transfer that enables new tasks to use general knowledgein the base adapter without being misled by diverse knowledge in task-specificadapters. Empirical evaluation and analysis indicate that our framework obtainscomparable performance under deployment-friendly model capacity.

Hierarchical Inductive Transfer for Continual Dialogue Learning