  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. We show that glyph-based models
are able to consistently outperform word/char ID-based models in a wide range
of Chinese NLP tasks. We are able to set new state-of-the-art results for a
variety of Chinese NLP tasks, including tagging (NER, CWS, POS), sentence pair
classification, single sentence classification tasks, dependency parsing, and
semantic role labeling. For example, the proposed model achieves an F1 score of
80.6 on the OntoNotes dataset of NER, +1.5 over BERT; it achieves an almost
perfect accuracy of 99.8\% on the Fudan corpus for text classification. Code
found at https://github.com/ShannonAI/glyce.
