  Current image captioning methods are usually trained via (penalized) maximum
likelihood estimation. However, the log-likelihood score of a caption does not
correlate well with human assessments of quality. Standard syntactic evaluation
metrics, such as BLEU, METEOR and ROUGE, are also not well correlated. The
newer SPICE and CIDEr metrics are better correlated, but have traditionally
been hard to optimize for. In this paper, we show how to use a policy gradient
(PG) method to directly optimize a linear combination of SPICE and CIDEr (a
combination we call SPIDEr): the SPICE score ensures our captions are
semantically faithful to the image, while CIDEr score ensures our captions are
syntactically fluent. The PG method we propose improves on the prior MIXER
approach, by using Monte Carlo rollouts instead of mixing MLE training with PG.
We show empirically that our algorithm leads to easier optimization and
improved results compared to MIXER. Finally, we show that using our PG method
we can optimize any of the metrics, including the proposed SPIDEr metric which
results in image captions that are strongly preferred by human raters compared
to captions generated by the same model but trained to optimize MLE or the COCO
metrics.
