Challenge: Recent work on learning from multiple tasks has shown that adding an extra fusion layer to implement knowledge composition is non-scalable for some applications.
Approach: They propose a two-stage knowledge distillation algorithm to extract task specific knowledge by using local data to train a student adapter.
Outcome: Experiments on frequently asked question retrieval in task-oriented dialog systems validate the efficiency of AdapterDistillation.

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