  Deep multi-task learning attracts much attention in recent years as it
achieves good performance in many applications. Feature learning is important
to deep multi-task learning for sharing common information among tasks. In this
paper, we propose a Hierarchical Graph Neural Network (HGNN) to learn augmented
features for deep multi-task learning. The HGNN consists of two-level graph
neural networks. In the low level, an intra-task graph neural network is
responsible of learning a powerful representation for each data point in a task
by aggregating its neighbors. Based on the learned representation, a task
embedding can be generated for each task in a similar way to max pooling. In
the second level, an inter-task graph neural network updates task embeddings of
all the tasks based on the attention mechanism to model task relations. Then
the task embedding of one task is used to augment the feature representation of
data points in this task. Moreover, for classification tasks, an inter-class
graph neural network is introduced to conduct similar operations on a finer
granularity, i.e., the class level, to generate class embeddings for each class
in all the tasks use class embeddings to augment the feature representation.
The proposed feature augmentation strategy can be used in many deep multi-task
learning models. we analyze the HGNN in terms of training and generalization
losses. Experiments on real-world datastes show the significant performance
improvement when using this strategy.
