We consider grounding open domain dialogues with images. Existing work assumes that both an image and a textual context are available, but image-grounded dialogues by nature are more difficult to obtain than textual dialogues. Thus, we propose learning a response generation model with both image-grounded dialogues and textual dialogues by assuming that there is a latent variable in a textual dialogue that represents the image, and trying to recover the latent image through text-to-image generation techniques. The likelihood of the two types of dialogues is then formulated by a response generator and an image reconstructor that are learned within a conditional variational auto-encoding framework. Empirical studies are conducted in both image-grounded conversation and text-based conversation. In the first scenario, image-grounded dialogues, especially under a low-resource setting, can be effectively augmented by textual dialogues with latent images; while in the second scenario, latent images can enrich the content of responses and at the same time keep them relevant to contexts.

本文提出使用图像来增强样本数量的方法，以学习针对含有图像信息的对话的应答生成模型，并利用条件变分自编码框架下的响应生成器和图像重构器来刻画图像和文本信息之间的关系。实验表明，该方法在低资源情况下可以有效增加图像驱动类对话的训练样本，同时保持应答内容与上下文的相关性。

具有隐变量图像的开放域对话生成