FEVER is a publicly available dataset for fact extraction and verification against textual sources.
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FEVEROUS (Fact Extraction and VERification Over Unstructured and Structured information) is a fact verification dataset which consists of 87,026 verified claims. Each claim is annotated with evidence in the form of sentences and/or cells from tables in Wikipedia, as well as a label indicating whether this evidence supports, refutes, or does not provide enough information to reach a verdict.
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A testbed for commonsense reasoning about entity knowledge, bridging fact-checking about entities with commonsense inferences.
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Fact-checking (FC) articles which contains pairs (multimodal tweet and a FC-article) from politifact.com.
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FaVIQ (Fact Verification from Information-seeking Questions) is a challenging and realistic fact verification dataset that reflects confusions raised by real users. We use the ambiguity in information-seeking questions and their disambiguation, and automatically convert them to true and false claims. These claims are natural, and require a complete understanding of the evidence for verification. FaVIQ serves as a challenging benchmark for natural language understanding, and improves performance in professional fact checking.
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FACTIFY is a dataset on multi-modal fact verification. It contains images, textual claim, reference textual documenta and image. The task is to classify the claims into support, not-enough-evidence and refute categories with the help of the supporting data. We aim to combat fake news in the social media era by providing this multi-modal dataset. Factify contains 50,000 claims accompanied with 100,000 images, split into training, validation and test sets.
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A large-scale dataset that consists of 21,184 claims, where each claim is assigned a truthfulness label and ruling statement, with 58,523 pieces of evidence in the form of text and images. It supports the end-to-end multimodal fact-checking and explanation generation, where the input is a claim and a large collection of web sources, including articles, images, videos, and tweets, and the goal is to assess the truthfulness of the claim by retrieving relevant evidence and predicting a truthfulness label (i.e., support, refute and not enough information), and generate a rationalization statement to explain the reasoning and ruling process.
Intermediate annotations from the FEVER dataset that describe original facts extracted from Wikipedia and the mutations that were applied, yielding the claims in FEVER.
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