An Investigation of the Interactions Between Pre-Trained Word Embeddings, Character Models and POS Tags in Dependency Parsing
Table 3: Mean LAS across nine languages for a baseline system employing randomly-initialised word embeddings only, compared to three separate systems using pre-trained word embeddings (+ext), a character model (+char), and POS tags (+pos). Scores are also shown for a combined system that utilises all three techniques and corresponding systems where one of the three techniques is ablated (−ext, −char and −pos).
['baseline', '67.7', 'combined', '81.0']
What immediately stands out in Fig. Once again improvement is universal for each system compared to the baseline. There are however substantial differences between languages. The three biggest overall improvements are for Finnish, Korean and Russian, with a particularly notable increase in the Korean case. This suggests that the baseline model struggles to learn adequate representations for each word type in these languages. It is also notable that the character model becomes more important compared to other methods for these three languages.