What do character-level models learn about morphology?The case of dependency parsing
Table 3: LAS improvements (char-lstm − word) for non-OOV and OOV words on development set.
['Language', 'dev %OOV', 'LAS improvement non-OOV', 'LAS improvement OOV']
On the agglutinative languages Finnish and Turkish, where the OOV rates are 23% and 24% respectively, we see the highest LAS improvements, and we see especially large improvements in accuracy of OOV words. However, the effects are more mixed in other languages, even with relatively high OOV rates. In particular, languages with rich morphology like Czech, Russian, and (unvocalised) Arabic see more improvement than languages with moderately rich morphology and high OOV rates like Portuguese or Spanish. This pattern suggests that parameter sharing between pairs of observed training words can also improve parsing performance. For example, if “dog” and “dogs” are observed in the training data, they will share activations in their context and on their common prefix.