  We propose a new approach to natural language understanding in which we
consider the input text as an image and apply 2D Convolutional Neural Networks
to learn the local and global semantics of the sentences from the variations
ofthe visual patterns of words. Our approach demonstrates that it is possible
to get semantically meaningful features from images with text without using
optical character recognition and sequential processing pipelines, techniques
that traditional Natural Language Understanding algorithms require. To validate
our approach, we present results for two applications: text classification and
dialog modeling. Using a 2D Convolutional Neural Network, we were able to
outperform the state-of-art accuracy results of non-Latin alphabet-based text
classification and achieved promising results for eight text classification
datasets. Furthermore, our approach outperformed the memory networks when using
out of vocabulary entities fromtask 4 of the bAbI dialog dataset.
