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Existing deep multitask learning (MTL) approaches align layers shared betweentasks in a parallel ordering. Such an organization significantly constricts thetypes of shared structure that can be learned. The necessity of parallelordering for deep MTL is first tested by comparing it with permuted ordering ofshared layers. The results indicate that a flexible ordering can enable moreeffective sharing, thus motivating the development of a soft ordering approach,which learns how shared layers are applied in different ways for differenttasks. Deep MTL with soft ordering outperforms parallel ordering methods acrossa series of domains. These results suggest that the power of deep MTL comesfrom learning highly general building blocks that can be assembled to meet thedemands of each task.

Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering