  Color names are often made up of multiple words. As a task in natural
language understanding we investigate in depth the capacity of neural networks
based on sums of word embeddings (SOWE), recurrence (LSTM and GRU based RNNs)
and convolution (CNN), to estimate colors from sequences of terms. We consider
both point and distribution estimates of color. We argue that the latter has a
particular value as there is no clear agreement between people as to what a
particular color describes -- different people have a different idea of what it
means to be ``very dark orange'', for example. Surprisingly, despite it's
simplicity, the sum of word embeddings generally performs the best on almost
all evaluations.
