  As sharing images in an instant message is a crucial factor, there has been
active research on learning an image-text multi-modal dialogue models. However,
training a well-generalized multi-modal dialogue model remains challenging due
to the low quality and limited diversity of images per dialogue in existing
multi-modal dialogue datasets. In this paper, we propose an automated pipeline
to construct a multi-modal dialogue dataset, ensuring both dialogue quality and
image diversity without requiring minimum human effort. In our pipeline, to
guarantee the coherence between images and dialogue, we prompt GPT-4 to infer
potential image-sharing moments - specifically, the utterance, speaker,
rationale, and image description. Furthermore, we leverage CLIP similarity to
maintain consistency between aligned multiple images to the utterance. Through
this pipeline, we introduce DialogCC, a high-quality and diverse multi-modal
dialogue dataset that surpasses existing datasets in terms of quality and
diversity in human evaluation. Our comprehensive experiments highlight that
when multi-modal dialogue models are trained using our dataset, their
generalization performance on unseen dialogue datasets is significantly
enhanced. We make our source code and dataset publicly available.
