When is multitask learning effective? Semantic sequence prediction under varying data conditions
Table 3: Comparison different POS variants (data source/tag granularity): Baseline (BL) and the difference in performance on the +POS system when using the UD Corpus with UPOS (UD/UPOS) or with PTB tabs (UD/PTB), as well as the Wall Street Journal with PTB tags (WSJ/PTB).
['[EMPTY]', 'BL', 'ΔUD/UPOS', 'ΔUD/PTB', 'ΔWSJ/PTB']
Therefore we have also measured the effect of using different sources of Pos auxiliary data to give account for the possible differences in label inventory and corpus for all tasks, high and low-level, cf. We have used the PTB version of the English UD corpus (UD/PTB) as well as the training section of the Wall Street Journal (WSJ) treebank as of Pos (WSJ/PTB) auxiliary task. The former offers the opportunity to change the Pos inventory to the three times larger PTB inventory while using the same corpus. However, the characteristics of the UD/UPOS we have used as Pos throughout the article makes it a more suitable auxiliary source, in fact it systematically outperforms the other two. We argue that UD/UPOS has enough linguistic signal to be a useful auxiliary task, while still depending on a smaller label inventory. Interestingly, if we use Pos for Chunk (cf.