Glyce: Glyph-vectors for Chinese Character Representations
Table 10: Impact of the auxilliary image classification training objective.
['Strategy', 'P', 'R', 'F', 'Acc']
We also explore the influence of the image-classification training objective, which outputs the glyph representation to an image-classification objective. As can be seen, this auxiliary training objective given a +0.8 performance boost. As can be seen, the adoption of tianzige-CNN structure introduces a performance boost of F1 about +1.0. Directly using deep CNNs in our task results in very poor performances because of (1) relatively smaller size of the character images: the size of ImageNet images is usually at the scale of 800*600, while the size of Chinese character images is significantly smaller, usually at the scale of 12*12; and (2) the lack of training examples: classifications on the ImageNet dataset utilizes tens of millions of different images. In contrast, there are only about 10,000 distinct Chinese characters. We utilize the Tianzige-CNN (田字格) structures tailored to logographic character modeling for Chinese. This tianzige structure is of significant importance in extracting character meanings.