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FEVER 2.0 (2019)

James Thorne, Andreas Vlachos, Oana Cocarascu, Christos Christodoulopoulos, Arpit Mittal

2019

We present the results of the second Fact Extraction and VERification (FEVER2.0) Shared Task. The task challenged participants to both build systems to verify factoid claims using evidence retrieved from Wikipedia and to generate adversarial attacks against other participant’s systems. The shared task had three phases: building, breaking and fixing. There were 8 systems in the builder’s round, three of

Conversational AI
Amazon helps launch workshop on automatic fact verification

Larry Hardesty

October 25, 2018

At the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), Amazon researchers and their colleagues at the University of Sheffield and Imperial College London will host the first Workshop on Fact Extraction and Verification, which will explore how computer systems can learn to recognize false assertions online.

Search and information retrieval
Public release of fact-checking dataset quickly begins to pay dividends

Larry Hardesty

August 19, 2018

At the annual meeting of the North American chapter of the Association for Computational Linguistics in June, researchers at Amazon and the University of Sheffield released a new dataset that can be used to train machine-learning systems to determine the veracity of factual assertions online. The dataset is called FEVER, for fact extraction and verification.

Search and information retrieval
Amazon and University of Sheffield researchers make large-scale fact extraction and verification dataset publicly available

Arpit Mittal

May 04, 2018

In recent years, the amount of textual information produced daily has increased exponentially. This information explosion has been accelerated by the ease with which data can be shared across the web. Most of the textual information is generated as free-form text, and only a small fraction is available in structured format (Wikidata, Freebase etc.) that can be processed and analyzed directly by machines.

Search and information retrieval
FEVER (2018)

James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Arpit Mittal

2018

FEVER (Fact Extraction and VERification) consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims are classified as Supported, Refuted or NotEnoughInfo. For the first two classes, the annotators also recorded the sentence(s) forming the necessary evidence for their judgment.

Conversational AI
FEVER: a large-scale dataset for Fact Extraction and VERification

James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Arpit Mittal

NAACL 2018

2018

In this paper we introduce a new publicly available dataset for verification against textual sources, FEVER: Fact Extraction and VERification. It consists of 185,445 claims generated by altering sentences extracted from Wikipedia and subsequently verified without knowledge of the sentence they were derived from. The claims are classified as SUPPORTED, REFUTED or NOTENOUGHINFO by annotators achieving 0.6841

Related: The FEVER data set: What doesn’t kill it will make it stronger

Conversational AI
The Fact Extraction and VERification (FEVER) Shared Task

James Thorne, Andreas Vlachos, Oana Cocarascu, Christos Christodoulopoulos, Arpit Mittal

EMNLP 2018

2018

We present the results of the first Fact Extraction and VERification (FEVER) Shared Task. The task challenged participants to classify whether human-written factoid claims could be SUPPORTED or REFUTED using evidence retrieved from Wikipedia. We received entries from 23 competing teams, 19 of which scored higher than the previously published baseline. The best performing system achieved a FEVER score of

Conversational AI

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