Same Representation, Different Attentions:A New Scheme of Information Sharing for Multi-task Learning
Table 3: Results of first 6 tasks with multi-task learning and transfer learning
['[EMPTY]', 'SSP-MTL', 'PSP-MTL', 'SA-MTL', 'DA-MTL']
The multi-task learning results are derived by training the first 6 tasks in general multi-task learning. For transfer learning, we choose the last 10 tasks to train our model with multi-task learning, then the learned shared sentence encoding layer are kept frozen and transferred to train the first 6 tasks. The reason is that by using attention mechanism, richer information can be captured into the shared representation layer, thereby benefiting the other task.