Robust Task Clustering for Deep and DiverseMulti-Task and Few-Shot Learning
Table 3: Accuracy of Multi-Task Learning on the 1,491 dialog intent classification tasks.
['[BOLD] Model', '[BOLD] Avg Acc']
Compared to the results on the 175 tasks, the holistic MTL-CNN achieves larger improvement (6%) over the single-task CNNs, which is a stronger baseline. Similar as the observation on the 175 tasks, here the main reason for its improvement is the consistent development and test performance due to holistic multi-task training approach: both the single-task and holistic multi-task model achieve around 66% average accuracy on development sets. Instead, we only use the information of ∼4.5% of all the task-pairs, and our algorithm still achieves a significant improvement over the baselines.