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

Cross-modal Language Generation using Pivot Stabilization for Web-scale Language Coverage