  We consider generation and comprehension of natural language referring
expression for objects in an image. Unlike generic "image captioning" which
lacks natural standard evaluation criteria, quality of a referring expression
may be measured by the receiver's ability to correctly infer which object is
being described. Following this intuition, we propose two approaches to utilize
models trained for comprehension task to generate better expressions. First, we
use a comprehension module trained on human-generated expressions, as a
"critic" of referring expression generator. The comprehension module serves as
a differentiable proxy of human evaluation, providing training signal to the
generation module. Second, we use the comprehension module in a
generate-and-rerank pipeline, which chooses from candidate expressions
generated by a model according to their performance on the comprehension task.
We show that both approaches lead to improved referring expression generation
on multiple benchmark datasets.
