  Cross-modal language generation tasks such as image captioning are directly
hurt in their ability to support non-English languages by the trend of
data-hungry models combined with the lack of non-English annotations. We
investigate potential solutions for combining existing language-generation
annotations in English with translation capabilities in order to create
solutions at web-scale in both domain and language coverage. We describe an
approach called Pivot-Language Generation Stabilization (PLuGS), which
leverages directly at training time both existing English annotations (gold
data) as well as their machine-translated versions (silver data); at run-time,
it generates first an English caption and then a corresponding target-language
caption. We show that PLuGS models outperform other candidate solutions in
evaluations performed over 5 different target languages, under a large-domain
testset using images from the Open Images dataset. Furthermore, we find an
interesting effect where the English captions generated by the PLuGS models are
better than the captions generated by the original, monolingual English model.
