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

Learning of Colors from Color Names: Distribution and Point Estimation