  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 the visual
scene information at the time of a conversation can be represented by an image,
and trying to recover the latent images of the textual dialogues 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.
