  Given the advantage and recent success of English character-level and
subword-unit models in several NLP tasks, we consider the equivalent modeling
problem for Chinese. Chinese script is logographic and many Chinese logograms
are composed of common substructures that provide semantic, phonetic and
syntactic hints. In this work, we propose to explicitly incorporate the visual
appearance of a character's glyph in its representation, resulting in a novel
glyph-aware embedding of Chinese characters. Being inspired by the success of
convolutional neural networks in computer vision, we use them to incorporate
the spatio-structural patterns of Chinese glyphs as rendered in raw pixels. In
the context of two basic Chinese NLP tasks of language modeling and word
segmentation, the model learns to represent each character's task-relevant
semantic and syntactic information in the character-level embedding.
