论文标题
视觉信息指导零射击术产生
Visual Information Guided Zero-Shot Paraphrase Generation
论文作者
论文摘要
零镜头的释义引起了很大的关注，因为大规模的高质量释义语料库受到限制。反向翻译，也称为基于枢轴的方法，是典型的。几项作品利用不同的信息为“枢轴”，例如语言，语义表示等。在本文中，我们使用诸如反向翻译的“枢轴”之类的视觉信息进行了探索。与管道背面翻译方法不同，我们仅基于配对的图像捕获数据，提出了视觉信息引导的零摄像机释义（VIPG）。它共同训练图像字幕模型和释义模型，并利用图像字幕模型来指导释义模型的训练。自动评估和人类评估都表明，我们的模型可以以良好的相关性，流利性和多样性生成释义，而图像是零摄像机生成的一种有希望的枢轴。
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.
