Weakly Supervised POS TaggersPerform Poorly on Truly Low-Resource Languages
Table 3: High-resource POS tagging accuracy. “-”: comparable data to previous experiments; “+”: higher quality data.
['[BOLD] Language', '[BOLD] CHR11', '[BOLD] GAR13', '[BOLD] AMB', '[BOLD] FREQ']
Comparing the average results of the neural models with those for the low-resource languages, we find an improvement of 0.02 for AMB and 0.09 for FREQ, respectively. Thus, those approaches work slightly better for our high-resource languages, maybe because those are closer to English, our source language for cross-lingual transfer. However, when higher quality resources are used, tagging performance is even 0.31 higher for AMB. Similarly, CHR11 and GAR13 improve strongly over their results for the resource-poor languages. For FREQ, performance nearly doubles: it increases by 0.36. This highlights the importance of using high quality resources.