  Most existing deep multi-task learning models are based on parameter sharing,
such as hard sharing, hierarchical sharing, and soft sharing. How choosing a
suitable sharing mechanism depends on the relations among the tasks, which is
not easy since it is difficult to understand the underlying shared factors
among these tasks. In this paper, we propose a novel parameter sharing
mechanism, named \emph{Sparse Sharing}. Given multiple tasks, our approach
automatically finds a sparse sharing structure. We start with an
over-parameterized base network, from which each task extracts a subnetwork.
The subnetworks of multiple tasks are partially overlapped and trained in
parallel. We show that both hard sharing and hierarchical sharing can be
formulated as particular instances of the sparse sharing framework. We conduct
extensive experiments on three sequence labeling tasks. Compared with
single-task models and three typical multi-task learning baselines, our
proposed approach achieves consistent improvement while requiring fewer
parameters.
