What do character-level models learn about morphology?The case of dependency parsing
Table 5: Morphological tagging accuracy from representations using the char-lstm and oracle embedding and encoder representations in Czech. Baseline simply chooses the most frequent tag. All means we concatenate all annotated features in UD as one tag.
['Feature', 'baseline', 'embedding char', 'embedding oracle', 'encoder char', 'encoder oracle']
The oracle embeddings have almost perfect accuracy—and this is just what we expect, since the representation only needs to preserve information from its input. The char-lstm embeddings perform well on number and gender, but less well on case. This results suggest that the character-level models still struggle to learn case when given only the input text. Comparing the char-lstm with a baseline model which predicts the most frequent feature for each type in the training data, we observe that both of them show similar trends even though character models slightly outperforms the baseline model. In these three fusional languages, contextual information helps character-level model to predict the correct case. However, its performance still behind the oracle.