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FEVER: a Large-scale Dataset for Fact Extraction and VERification
TLDR
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.
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Stance Detection with Bidirectional Conditional Encoding
Stance detection is the task of classifying the attitude expressed in a text towards a target such as Hillary Clinton to be "positive", negative" or "neutral". Previous work has assumed that either…
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The Fact Extraction and VERification (FEVER) Shared Task
TLDR
The first Fact Extraction and VERification (FEVER) Shared Task challenged participants to classify whether human-written factoid claims could be SUPPORTED or REFUTED using evidence retrieved from Wikipedia.
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Overview of BioCreative II gene mention recognition
TLDR
It is demonstrated that, by combining the results from all submissions, an F score of 0.9066 is feasible, and furthermore that the best result makes use of the lowest scoring submissions.
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Emergent: a novel data-set for stance classification
TLDR
Emergent provides a real-world data source for a variety of natural language processing tasks in the context of fact-checking and uses a logistic regression classifier to develop features that examine the headline and its agreement with the claim.
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The FEVER2.0 Shared Task
TLDR
There was a great variety in adversarial attack types as well as the techniques used to generate the attacks, highlighting commonalities and innovations among participating systems.
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Fact Checking: Task definition and dataset construction
TLDR
The task of fact checking is introduced and the construction of a publicly available dataset using statements fact-checked by journalists available online is detailed, including baseline approaches for the task and the challenges that need to be addressed.
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Automated Fact Checking: Task Formulations, Methods and Future Directions
TLDR
This paper surveys automated fact checking research stemming from natural language processing and related disciplines, unifying the task formulations and methodologies across papers and authors, and highlights the use of evidence as an important distinguishing factor among them cutting across task formulation and methods.
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A stopping criterion for active learning
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Semantic Parsing as Machine Translation
TLDR
This work approaches semantic parsing as a straightforward machine translation task, and demonstrates that standard machine translation components can be adapted into a semantic parser that is competitive with the state of the art, and achieves higher accuracy than recently proposed purpose-built systems.
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