Character Sequence Models for Colorful Words

Kazuya Kawakami, Chris Dyer, Bryan R. Routledge, Noah A. Smith

Abstract: We present a neural network architecture to predict a point in color space from the sequence of characters in the color's name. Using large scale color--name pairs obtained from an online color design forum, we evaluate our model on a "color Turing test" and find that, given a name, the colors predicted by our model are preferred by annotators to color names created by humans. Our datasets and demo system are available online at colorlab.us.