What do character-level models learn about morphology?The case of dependency parsing
Table 2: Unlabeled Attachment Score (UAS) and Labeled Attachment Score (LAS) on test set. The best accuracy for each language is highlighted in bold for all models, and for all non-oracle models. o/c: LAS improvement from char-lstm to oracle.
['Model → ↓ Language', 'word UAS', 'word LAS', 'char-lstm UAS', 'char-lstm LAS', 'char-cnn UAS', 'char-cnn LAS', 'trigram-lstm UAS', 'trigram-lstm LAS', 'oracle UAS', 'oracle LAS', 'o/c LAS']
First, they support previous findings that character-level models outperform word-based models—indeed, the char-lstm model outperforms the word model on LAS for all languages except Hindi and Urdu for which the results are identical. Third, they demonstrate that character-level models rarely match the accuracy of an oracle model with access to explicit morphology. Character-level models are effective because they can represent OOV words and orthographic regularities of words that are consistent with morphology. But they depend on context to disambiguate words, and for some words this context is insufficient.