FEVER: a Large-scale Dataset for Fact Extraction and VERification
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Summary
This paper introduces a new publicly available dataset for verification against textual sources, FEVER, which consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from.
- Type
- preprint
- Published
- 2018-03-14
- Cited by
- 2,395
- References
- 42
- Access
- Open access
- OpenAlex
- https://openalex.org/W2789566302
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:4711425
Keywords
Computer science, Pipeline (software), Sentence, Testbed, Natural language processing
References
- Deriving a Web-Scale Common Sense Fact Database
- Good Question! Statistical Ranking for Question Generation
- A large annotated corpus for learning natural language inference
- Measuring nominal scale agreement among many raters.
- NaturalLI: Natural Logic Inference for Common Sense Reasoning
- Recognizing textual entailment: Rational, evaluation and approaches
- The Stanford CoreNLP Natural Language Processing Toolkit
- Open Language Learning for Information Extraction
- NLTK: The Natural Language Toolkit
- RCV1: A New Benchmark Collection for Text Categorization Research
- Fact Checking: Task definition and dataset construction
- Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning
- Emergent: a novel data-set for stance classification
- A Decomposable Attention Model for Natural Language Inference
- Reading Wikipedia to Answer Open-Domain Questions
- “Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection
- A simple but tough-to-beat baseline for the Fake News Challenge stance detection task
- Lies, Damn Lies and Viral Content
- Automated Historical Fact-Checking by Passage Retrieval, Word Statistics, and Virtual Question-Answering
- AllenNLP: A Deep Semantic Natural Language Processing Platform
Cited by
- Learning Class-specific Word Representations for Early Detection of Hoaxes in Social Media
- Integrating Stance Detection and Fact Checking in a Unified Corpus
- Automatic Stance Detection Using End-to-End Memory Networks
- TwoWingOS: A Two-Wing Optimization Strategy for Evidential Claim Verification
- KGCleaner : Identifying and Correcting Errors Produced by Information Extraction Systems
- Identifying the sentiment styles of YouTube’s vloggers
- Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging
- Predicting Factuality of Reporting and Bias of News Media Sources
- DeFactoNLP: Fact Verification using Entity Recognition, TFIDF Vector Comparison and Decomposable Attention
- UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification
- Scalable Micro-planned Generation of Discourse from Structured Data
- Teaching Syntax by Adversarial Distraction
- Team UMBC-FEVER : Claim verification using Semantic Lexical Resources
- Differentiable Greedy Networks
- SURFACE: Semantically Rich Fact Validation with Explanations
- Attentive Convolution: Equipping CNNs with RNN-style Attention Mechanisms
- The Fact Extraction and VERification (FEVER) Shared Task
- Technology-Enabled Disinformation: Summary, Lessons, and Recommendations
- Uni-DUE Student Team: Tackling fact checking through decomposable attention neural network
- Towards detecting deceptive intentions on a large scale
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