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DialogCC: An Automated Pipeline for Creating High-Quality Multi-Modal Dialogue Dataset

Computer Vision and Pattern Recognition 2024-04-01 v2 Computation and Language

Abstract

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.

Keywords

Cite

@article{arxiv.2212.04119,
  title  = {DialogCC: An Automated Pipeline for Creating High-Quality Multi-Modal Dialogue Dataset},
  author = {Young-Jun Lee and Byungsoo Ko and Han-Gyu Kim and Jonghwan Hyeon and Ho-Jin Choi},
  journal= {arXiv preprint arXiv:2212.04119},
  year   = {2024}
}

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NAACL 2024