We combine a neural image captioner with a Rational Speech Acts (RSA) model
to make a system that is pragmatically informative: its objective is to produce
captions that are not merely true but also distinguish their inputs from
similar images. Previous attempts to combine RSA with neural image captioning
require an inference which normalizes over the entire set of possible
utterances. This poses a serious problem of efficiency, previously solved by
sampling a small subset of possible utterances. We instead solve this problem
by implementing a version of RSA which operates at the level of characters
("a","b","c"...) during the unrolling of the caption. We find that the
utterance-level effect of referential captions can be obtained with only
character-level decisions. Finally, we introduce an automatic method for
testing the performance of pragmatic speaker models, and show that our model
outperforms a non-pragmatic baseline as well as a word-level RSA captioner.

本文提出了一种将神经图像字幕生成器与 Rational Speech Acts（RSA）模型相结合的系统，其核心目标是生成不仅真实而且能够将其输入与类似图像区分开的字幕。通过在字幕展开期间实现一种基于字符（“a”，“b”，“c”…）的 RSA 版本来解决以前的计算效率问题，同时提出了一种自动测试质询演讲者模型性能的方法，并表明我们的模型优于非语用基线，以及单词级 RSA 字幕生成器。