Robust Task Clustering for Deep and DiverseMulti-Task and Few-Shot Learning
(b) FSL setting (one-shot + 20 examples).
['[BOLD] Model', '[BOLD] Avg Acc']
Note that the holistic MTL methods achieve much better results compared to single-task CNNs. This is because the tasks usually have smaller training and development sets, and both the model parameters learned on training set and the hyper-parameters selected on development set can easily lead to over-fitting. RobustTC-MTL achieves large improvement (5.5%) over the best MTL baseline, because the tasks here are more diverse than the sentiment classification tasks and task-clustering greatly reduces conflicts from irrelevant tasks.