What can we learn from Semantic Tagging?
Table 1: Results for single-task models (ST), fully-shared networks (FSN), partially-shared networks (PSN), and learning what to share (LWS). All scores are reported as accuracy, except UD DEP for which we report LAS/UAS F1 score.
['[BOLD] Model', '[BOLD] SNLI', '[BOLD] SICK-E', '[BOLD] UPOS', '[BOLD] UD DEP']
In line with Bjerva et al. ’s findings, the FSN setting leads to an improvement for UPOS tagging. POS tagging, a sequence labeling task, can be seen as the most closely related to semantic tagging, therefore negative transfer is minimal and the full sharing of parameters is beneficial. Surprisingly, the FSN setting also leads improvements for UD DEP. Indeed, for UD DEP, all of the MTL models outperform the ST model by increasing margins. For the NLI tasks, however, there is a clear degradation in performance.