Title: Learning Distributions of Meant Color,
Abstract: When a speaker says the name of a color, the color that they picture is not
necessarily the same as the listener imagines. Color is a grounded semantic
task, but that grounding is not a mapping of a single word (or phrase) to a
single point in color-space. Proper understanding of color language requires
the capacity to map a sequence of words to a probability distribution in
color-space. A distribution is required 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'. We propose a novel
GRU-based model to handle this case. Learning how each word in a color name
contributes to the color described, allows for knowledge sharing between uses
of the words in different color names. This knowledge sharing significantly
improves predicative capacity for color names with sparse training data. The
extreme case of this challenge in data sparsity is for color names without any
direct training data. Our model is able to predict reasonable distributions for
these cases, as evaluated on a held-out dataset consisting only of such terms.