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Datasets:
Dzeniks
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hover

Tasks:
Text Classification
Modalities:
Tabular
Text
Formats:
json
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metadata
license: mit
task_categories:
  - text-classification

Hover Dataset

The Hover dataset is a collection of labeled examples for many-hop fact extraction and claim verification tasks. It contains claims, with each claim labeled as either "Supports" or "Refutes". The dataset was created by Yichen Jiang, Shikha Bordia, Zheng Zhong, Charles Dognin, Maneesh Singh, and Mohit Bansal, and was presented in their paper "HoVer: A Dataset for Many-Hop Fact Extraction and Claim Verification" at the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) Hover page.

Format

The Hover dataset is formatted as a TSV file, with each line containing the following fields:

  • Claim: The text of the claim to be verified.
  • Label: The label for the claim, either "0" for "Supports" or "1" for "Refutes".
  • Explanation: A sentence or phrase explaining why the claim is labeled as such.
  • Evidence: Evidence supporting or refuting the claim, if available. This may be a URL or a short text snippet.