Hierarchical Character Embeddings: Learning Phonological and Semantic Representations in Languages of Logographic Origin using Recursive Neural Networks
Table VII: Language modeling performance on test sets from different datasets. hier-emb: hierarchical embedding, glyph-emb: glyph embeddings, baseline: standard embeddings, ext: additional bias term in treeLSTM. Results for the LSTM, Segmental Neural LM, and the glyph embeddings were taken from the original papers. We also reimplemented the glyph embeddings for a fairer comparison.
['Model', 'Perplexity', 'BPC']
Thus, we reimplemented the glyph embedding for a fairer comparison. We only include the results for the Segmental Neural LM model for reference and did not reimplement this model because it depends on multitask training which is different from the other models. The hierarchical embeddings outperformed the standard embeddings in all datasets, regardless of whether the datasets use simplified or traditional characters.