An Investigation of the Interactions Between Pre-Trained Word Embeddings, Character Models and POS Tags in Dependency Parsing
Table 6: Mean LAS across nine languages for baseline system compared to systems with POS tags predicted by different systems. Comparison also shown for systems employing pre-trained word vectors and a character vector.
['baseline', '67.7', '−pos', '80.3']
We see a clear improvement from UDPipe to Stanford and then from Stanford to gold tags over the baseline system. This partially confirms results from Dozat et al. More surprising perhaps is the result when comparing to the −pos system, which also makes use of pre-trained word embeddings and a character model. Here, results do not improve at all by adding predicted tags from UDPipe. Stanford tags do give an improvement of 0.7 LAS over −pos, but this is a long way from the improvement of 8.2 LAS we see when adding them on top of baseline. Gold tags do however still give a big improvement over −pos (3.5 LAS), suggesting strongly that both UDPipe and Stanford struggle with the decisions that would be most beneficial to parsing accuracy.