Hierarchical Character Embeddings: Learning Phonological and Semantic Representations in Languages of Logographic Origin using Recursive Neural Networks
Table VIII: Number of times the model using hierarchical embeddings predicts the phonetic sub-unit is on the right of a logograph that follows the left-right arrangement. The scenarios were described in Table I. Tr: Traditional, Sp: Simplified.
['Scenario', 'Left-Right', 'Prefer Right']
The third column shows the number of logographs following the left-right arrangement in which the right child is preferred over the left child. The hierarchical embeddings prefer the right child most of the time (close to 100%) in all three scenarios. Thus, the learned hierarchical embeddings consider the right sub-units to be more relevant for pronunciation prediction for the majority of compound logographs with the left-right arrangement. This is consistent with human intuition. Since human depends on this intuition to infer pronunciation and it seems to work well, this suggests that the hierarchical embeddings might have learned a general solution that works well.