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Natural Language Generation (NLG) accepts input data in the form of images,videos, or text and generates corresponding natural language text as output.Existing NLG methods mainly adopt a supervised approach and rely heavily oncoupled data-to-text pairs. However, for many targeted scenarios and fornon-English languages, sufficient quantities of labeled data are often notavailable. To relax the dependency on labeled data of downstream tasks, wepropose an intuitive and effective zero-shot learning framework, ZeroNLG, whichcan deal with multiple NLG tasks, including image-to-text (image captioning),video-to-text (video captioning), and text-to-text (neural machinetranslation), across English, Chinese, German, and French within a unifiedframework. ZeroNLG does not require any labeled downstream pairs for training.During training, ZeroNLG (i) projects different domains (across modalities andlanguages) to corresponding coordinates in a shared common latent space; (ii)bridges different domains by aligning their corresponding coordinates in thisspace; and (iii) builds an unsupervised multilingual auto-encoder to learn togenerate text by reconstructing the input text given its coordinate in sharedlatent space. Consequently, during inference, based on the data-to-textpipeline, ZeroNLG can generate target sentences across different languagesgiven the coordinate of input data in the common space. Within this unifiedframework, given visual (imaging or video) data as input, ZeroNLG can performzero-shot visual captioning; given textual sentences as input, ZeroNLG canperform zero-shot machine translation. We present the results of extensiveexperiments on twelve NLG tasks, showing that, without using any labeleddownstream pairs for training, ZeroNLG generates high-quality and believableoutputs and significantly outperforms existing zero-shot methods.

ZeroNLG: Aligning and Autoencoding Domains for Zero-Shot Multimodal and Multilingual Natural Language Generation