@InProceedings{liu-EtAl:2017:Long3,
  author    = {Liu, Frederick  and  Lu, Han  and  Lo, Chieh  and  Neubig, Graham},
  title     = {Learning Character-level Compositionality with Visual Features},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {2059--2068},
  abstract  = {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 topical content which resulting in embeddings
	that are coherent in visual space.},
  url       = {http://aclweb.org/anthology/P17-1188}
}