Zero-shot paraphrase generation has drawn much attention as the large-scale
high-quality paraphrase corpus is limited. Back-translation, also known as the
pivot-based method, is typical to this end. Several works leverage different
information as "pivot" such as language, semantic representation and so on. In
this paper, we explore using visual information such as image as the "pivot" of
back-translation. Different with the pipeline back-translation method, we
propose visual information guided zero-shot paraphrase generation (ViPG) based
only on paired image-caption data. It jointly trains an image captioning model
and a paraphrasing model and leverage the image captioning model to guide the
training of the paraphrasing model. Both automatic evaluation and human
evaluation show our model can generate paraphrase with good relevancy, fluency
and diversity, and image is a promising kind of pivot for zero-shot paraphrase
generation.

本文提出了一种基于视觉信息的零样本释义生成模型 ViPG，它利用图像的引导作用，通过联合训练图像字幕模型和释义模型，生成具有良好相关性、流畅性和多样性的释义，表明图像作为零样本释义的一种引导方式具有潜在前景。