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arXiv · 1606.03821

Learning to Generate Compositional Color Descriptions

Will Monroe·Noah D. Goodman·Christopher Potts
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Abstract

The production of color language is essential for grounded language generation. Color descriptions have many challenging properties: they can be vague, compositionally complex, and denotationally rich. We present an effective approach to generating color descriptions using recurrent neural networks and a Fourier-transformed color representation. Our model outperforms previous work on a conditional language modeling task over a large corpus of naturalistic color descriptions. In addition, probing the model's output reveals that it can accurately produce not only basic color terms but also descriptors with non-convex denotations ("greenish"), bare modifiers ("bright", "dull"), and compositional phrases ("faded teal") not seen in training.

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Will Monroe, Noah D. Goodman, Christopher Potts. 2016-10-18. Learning to Generate Compositional Color Descriptions. https://arxiv.org/abs/1606.03821

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