metadata

language:
  - en
license: apache-2.0
multilinguality:
  - monolingual
size_categories:
  - 100K<n<1M
tags:
  - chart
  - plot
  - chart-to-text
  - vistext
  - statista
  - pew
  - chart-visual-entailment
  - chart-understanding
  - chart-captioning
  - chart-summarization
  - document-image
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: dev
        path: data/dev-*
dataset_info:
  features:
    - name: image
      dtype: string
    - name: sentence
      dtype: string
    - name: label
      dtype: string
    - name: manipulation_type
      dtype: string
    - name: dataset
      dtype: string
  splits:
    - name: train
      num_bytes: 118229163
      num_examples: 522531
    - name: dev
      num_bytes: 9400046
      num_examples: 36002
  download_size: 51634467
  dataset_size: 127629209

Dataset Card for ChartVE's Training Data

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}
}