Datasets:
metadata
dataset_info:
features:
- name: id
dtype: string
- name: image
dtype: image
- name: image_filename
dtype: string
- name: url
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: relevant
sequence: int64
- name: bounding_boxes
list:
- name: id
dtype: int64
- name: ocr_info
list:
- name: bbox
struct:
- name: height
dtype: int64
- name: width
dtype: int64
- name: x
dtype: int64
- name: 'y'
dtype: int64
- name: confidence
dtype: int64
- name: word
dtype: string
- name: shape
struct:
- name: height
dtype: int64
- name: width
dtype: int64
- name: x
dtype: int64
- name: 'y'
dtype: int64
- name: structure
dtype: string
splits:
- name: train
num_bytes: 2057078991.77
num_examples: 21015
- name: val
num_bytes: 278965434.079
num_examples: 2839
- name: test
num_bytes: 1559157217.12
num_examples: 6708
download_size: 2271456645
dataset_size: 3895201642.969
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
task_categories:
- visual-question-answering
- question-answering
language:
- en
size_categories:
- 10K<n<100K
extra_gated_prompt: >-
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EXHIBIT A
The software and related data include the following files,
- data
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VisualMRC
VisualMRC: Machine Reading Comprehension on Document Images
VisualMRC is a visual machine reading comprehension dataset that proposes a task: given a question and a document image, a model produces an abstractive answer.
Citation and contact
If you use this dataset, please cite our work:
@inproceedings{VisualMRC2021,
author = {Ryota Tanaka and
Kyosuke Nishida and
Sen Yoshida},
title = {VisualMRC: Machine Reading Comprehension on Document Images},
booktitle = {AAAI},
year = {2021}
}