README.md

metadata

annotations_creators:
  - crowdsourced
language_creators:
  - crowdsourced
language:
  - en
license:
  - cc-by-4.0
multilinguality:
  - monolingual
size_categories:
  - 100K<n<1M
source_datasets:
  - original
task_categories:
  - text-classification
task_ids:
  - fact-checking
paperswithcode_id: tabfact
pretty_name: TabFact
dataset_info:
  - config_name: tab_fact
    features:
      - name: id
        dtype: int32
      - name: table_id
        dtype: string
      - name: table_text
        dtype: string
      - name: table_caption
        dtype: string
      - name: statement
        dtype: string
      - name: label
        dtype:
          class_label:
            names:
              '0': refuted
              '1': entailed
    splits:
      - name: train
        num_bytes: 99852664
        num_examples: 92283
      - name: validation
        num_bytes: 13846872
        num_examples: 12792
      - name: test
        num_bytes: 13493391
        num_examples: 12779
    download_size: 196508436
    dataset_size: 127192927
  - config_name: blind_test
    features:
      - name: id
        dtype: int32
      - name: table_id
        dtype: string
      - name: table_text
        dtype: string
      - name: table_caption
        dtype: string
      - name: statement
        dtype: string
      - name: test_id
        dtype: string
    splits:
      - name: test
        num_bytes: 10954442
        num_examples: 9750
    download_size: 196508436
    dataset_size: 10954442

Dataset Card for TabFact

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: TabFact
• Repository: GitHub
• Paper: TabFact: A Large-scale Dataset for Table-based Fact Verification
• Leaderboard: Leaderboard
• Point of Contact: Wenhu Chen

Dataset Summary

The problem of verifying whether a textual hypothesis holds the truth based on the given evidence, also known as fact verification, plays an important role in the study of natural language understanding and semantic representation. However, existing studies are restricted to dealing with unstructured textual evidence (e.g., sentences and passages, a pool of passages), while verification using structured forms of evidence, such as tables, graphs, and databases, remains unexplored. TABFACT is large scale dataset with 16k Wikipedia tables as evidence for 118k human annotated statements designed for fact verification with semi-structured evidence. The statements are labeled as either ENTAILED or REFUTED. TABFACT is challenging since it involves both soft linguistic reasoning and hard symbolic reasoning.

Supported Tasks and Leaderboards

[More Information Needed]

Languages

[More Information Needed]

Dataset Structure

Data Instances

[More Information Needed]

Data Fields

[More Information Needed]

Data Splits

[More Information Needed]

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

[More Information Needed]

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

[More Information Needed]

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

@inproceedings{2019TabFactA,
  title={TabFact : A Large-scale Dataset for Table-based Fact Verification},
  author={Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou and William Yang Wang},
  booktitle = {International Conference on Learning Representations (ICLR)},
  address = {Addis Ababa, Ethiopia},
  month = {April},
  year = {2020}
}

Contributions

Thanks to @patil-suraj for adding this dataset.