Previous work has modeled the compositionality of words by creating character-level models of meaning, reducing problems of sparsity for rare words. However, in many writing systems compositionality has an effect even on the character-level: the meaning of a character is derived by the sum of its parts. In this paper, we model this effect by creating embeddings for characters based on their visual characteristics, creating an image for the character and running it through a convolutional neural network to produce a visual character embedding. Experiments on a text classification task demonstrate that such model allows for better processing of instances with rare characters in languages such as Chinese, Japanese, and Korean. Additionally, qualitative analyses demonstrate that our proposed model learns to focus on the parts of characters that carry semantic content, resulting in embeddings that are coherent in visual space.

本文提出了一种针对汉字、日语、韩语等语言内所含罕见字符提高识别准确度的模型，它基于字符的构成并通过卷积神经网络生成视觉上的字符嵌入。实验结果表明该模型可更好地处理具有稀有字符的语言文本，并且能够学习集中于传达语义信息的字符部件，从而生成具有视觉一致性的字符嵌入。

利用视觉特征学习字级组合性