khhuang/chartve_dataset

Dataset Card for ChartVE's Training Data

• Dataset Description
• Paper Information
• Citation

Dataset Description

ChartVE (Chart Visual Entailment) is a visual entailment model introduced in the paper "Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning" for evaluating the factuality of a generated caption sentence with regard to the input chart. The model takes in a chart figure and a caption sentence as input, and outputs an entailment probability. This repository hosts the training and validation data for ChartVE.

Fields

Below, we illustrate the fields in each instance.

• image: The path to chart image. Images can be found in image.zip.
• sentence: The sentence used as the hypothesis.
• label: An indicator about whether the chart entails the given sentence.
• manipulation_type: The type of perturbation that alters the original sentence (this is only applicable for non-entailment instances).
• dataset: The source dataset of the chart image.

Paper Information

• Paper: https://arxiv.org/abs/2312.10160
• Code: https://github.com/khuangaf/CHOCOLATE/
• Project: https://khuangaf.github.io/CHOCOLATE

Citation

If you use the ChartVE dataset/model in your work, please kindly cite the paper using this BibTeX:

@misc{huang-etal-2023-do,
    title = "Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning",
    author = "Huang, Kung-Hsiang  and
      Zhou, Mingyang and
      Chan, Hou Pong  and
      Fung, Yi R. and
      Wang, Zhenhailong and
      Zhang, Lingyu and
      Chang, Shih-Fu and
      Ji, Heng",
    year={2023},
    eprint={2312.10160},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}