  We present two architectures for multi-task learning with neural sequence
models. Our approach allows the relationships between different tasks to be
learned dynamically, rather than using an ad-hoc pre-defined structure as in
previous work. We adopt the idea from message-passing graph neural networks and
propose a general \textbf{graph multi-task learning} framework in which
different tasks can communicate with each other in an effective and
interpretable way. We conduct extensive experiments in text classification and
sequence labeling to evaluate our approach on multi-task learning and transfer
learning. The empirical results show that our models not only outperform
competitive baselines but also learn interpretable and transferable patterns
across tasks.
