  Developers of text generation models rely on automated evaluation metrics as
a stand-in for slow and expensive manual evaluations. However, image captioning
metrics have struggled to give accurate learned estimates of the semantic and
pragmatic success of output text. We address this weakness by introducing the
first discourse-aware learned generation metric for evaluating image
descriptions. Our approach is inspired by computational theories of discourse
for capturing information goals using coherence. We present a dataset of
image$\unicode{x2013}$description pairs annotated with coherence relations. We
then train a coherence-aware metric on a subset of the Conceptual Captions
dataset and measure its effectiveness$\unicode{x2014}$its ability to predict
human ratings of output captions$\unicode{x2014}$on a test set composed of
out-of-domain images. We demonstrate a higher Kendall Correlation Coefficient
for our proposed metric with the human judgments for the results of a number of
state-of-the-art coherence-aware caption generation models when compared to
several other metrics including recently proposed learned metrics such as
BLEURT and BERTScore.
