Character Sequence Models for Colorful Words

Kazuya Kawakami, Chris Dyer, Bryan Routledge, Noah A. Smith
2016 Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing  
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 http: //colorlab.us.
doi:10.18653/v1/d16-1202 dblp:conf/emnlp/KawakamiDRS16 fatcat:7miapytb7rehpbpqsejphrella