Adversarial Multi-task Learning for Text Classification
Table 3: Error rates of our models on 16 datasets against vanilla multi-task learning. ϕ (Books) means that we transfer the knowledge of the other 15 tasks to the target task Books.
['[BOLD] Source Tasks', '[BOLD] Single Task LSTM', '[BOLD] Single Task BiLSTM', '[BOLD] Single Task sLSTM', '[BOLD] Single Task Avg.', '[BOLD] Transfer Models SP-MTL-SC', '[BOLD] Transfer Models SP-MTL-BC', '[BOLD] Transfer Models ASP-MTL-SC', '[BOLD] Transfer Models ASP-MTL-BC']
, we can see the shared layer from ASP-MTL achieves a better performance compared with SP-MTL. Besides, for the two kinds of transfer strategies, the Bi-Channel model performs better. The reason is that the task-specific layer introduced in the Bi-Channel model can store some private features. Overall, the results indicate that we can save the existing knowledge into a shared recurrent layer using adversarial multi-task learning, which is quite useful for a new task.