Multi-Task Learning for Sequence Tagging: An Empirical Study
Table 6: F1 scores for Multi-Dec. We compare All with All-but-one settings (All - ⟨task⟩). We test on each task in the columns. Beneficial settings are in green. Harmful setting are in red.
['[EMPTY]', 'upos', 'xpos', 'chunk', 'ner', 'mwe', 'sem', 'semtr', 'supsense', 'com', 'frame', 'hyp', '#↑', '#↓']
How much does one particular task contribute to the performance of All MTL? To investigate this, we remove one task at a time and train the rest jointly. We find that upos, sem and semtr are in general sensitive to a task being removed from All MTL. Moreover, at least one task significantly contributes to the success of All MTL at some point; if we remove it, the performance will drop. On the other hand, com generally negatively affects the performance of All MTL as removing it often leads to performance improvement.