Glyce: Glyph-vectors for Chinese Character Representations

Understand

It is intuitive that NLP tasks for logographic languages like Chinese should benefit from the use of the glyph information in those languages.

• However, due to the lack of rich pictographic evidence in glyphs and the weak generalization ability of standard computer vision models on character data, an effective way to utilize the glyph information remains to be found.
• In this paper, we address this gap by presenting Glyce, the glyph-vectors for Chinese character representations.
• We make three major innovations: (1) We use historical Chinese scripts (e.g., bronzeware script, seal script, traditional Chinese, etc) to enrich the pictographic evidence in characters; (2) We design CNN structures (called tianzege-CNN) tailored to Chinese character image processing; and (3) We use image-classification as an auxiliary task in a multi-task learning setup to increase the model's ability to generalize.