  A key assumption in multi-task learning is that at the inference time the
multi-task model only has access to a given data point but not to the data
point's labels from other tasks. This presents an opportunity to extend
multi-task learning to utilize data point's labels from other auxiliary tasks,
and this way improves performance on the new task. Here we introduce a novel
relational multi-task learning setting where we leverage data point labels from
auxiliary tasks to make more accurate predictions on the new task. We develop
MetaLink, where our key innovation is to build a knowledge graph that connects
data points and tasks and thus allows us to leverage labels from auxiliary
tasks. The knowledge graph consists of two types of nodes: (1) data nodes,
where node features are data embeddings computed by the neural network, and (2)
task nodes, with the last layer's weights for each task as node features. The
edges in this knowledge graph capture data-task relationships, and the edge
label captures the label of a data point on a particular task. Under MetaLink,
we reformulate the new task as a link label prediction problem between a data
node and a task node. The MetaLink framework provides flexibility to model
knowledge transfer from auxiliary task labels to the task of interest. We
evaluate MetaLink on 6 benchmark datasets in both biochemical and vision
domains. Experiments demonstrate that MetaLink can successfully utilize the
relations among different tasks, outperforming the state-of-the-art methods
under the proposed relational multi-task learning setting, with up to 27%
improvement in ROC AUC.
