Hierarchical Character Embeddings: Learning Phonological and Semantic Representations in Languages of Logographic Origin using Recursive Neural Networks
Table VI: Number of sentences in the training, validation, and test sets in each of the datasets.
['Dataset', 'Training', 'Validation', 'Test']
As the characters (logographs) in the output of language models are not independent, it is difficult to design meaningful statistical tests to evaluate the effectiveness of our proposed approach. Instead we chose a wide variety of five different datasets, consisting of three datasets using simplified characters (Chinese Penn Treebank (CTB) Version 5.1 If we can show consistent improvements across these datasets, it implies the proposed hierarchical embeddings are effective.