Multi-Task Networks With Universe, Group, and Task Feature Learning
Table 5: Results on benchmark dataset (ATIS and subsets of Snips).
['[BOLD] Model', '[BOLD] Snips-music Intent', '[BOLD] Snips-music Slot', '[BOLD] Snips-creative Intent', '[BOLD] Snips-creative Slot']
We now have four tasks: ATIS, Snips-location, Snips-music, and Snips-creative. Joint-SF-IC is our baseline that treats these four tasks independently. All other models process the four tasks together in the MTL setup. For the models introduced in this paper, we define two task groups: ATIS and Snips-location as one group, and Snips-music and Snips-creative as another. Our models, which use these groups, generally outperform the other MTL models (Parallel[Univ] and Parallel[Univ+Task]); especially the serial MTL architectures perform well.