  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.
