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

Young-Jun Lee, Byungsoo Ko, Han-Gyu Kim, Jonghwan Hyeon, Ho-Jin Choi


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 (https://dialogcc.github.io/).
Anthology ID:
2024.naacl-long.108
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1938–1963
Language:
URL:
https://aclanthology.org/2024.naacl-long.108
DOI:
Bibkey:
Cite (ACL):
Young-Jun Lee, Byungsoo Ko, Han-Gyu Kim, Jonghwan Hyeon, and Ho-Jin Choi. 2024. DialogCC: An Automated Pipeline for Creating High-Quality Multi-Modal Dialogue Dataset. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 1938–1963, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
DialogCC: An Automated Pipeline for Creating High-Quality Multi-Modal Dialogue Dataset (Lee et al., NAACL 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.naacl-long.108.pdf
Copyright:
 2024.naacl-long.108.copyright.pdf