  Designing shared neural architecture plays an important role in multi-task
learning. The challenge is that finding an optimal sharing scheme heavily
relies on the expert knowledge and is not scalable to a large number of diverse
tasks. Inspired by the promising work of neural architecture search (NAS), we
apply reinforcement learning to automatically find possible shared architecture
for multi-task learning. Specifically, we use a controller to select from a set
of shareable modules and assemble a task-specific architecture, and repeat the
same procedure for other tasks. The controller is trained with reinforcement
learning to maximize the expected accuracies for all tasks. We conduct
extensive experiments on two types of tasks, text classification and sequence
labeling, which demonstrate the benefits of our approach.

Artificial Intelligence;Computation and Language