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FEVER: a Large-scale Dataset for Fact Extraction and VERification
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.
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
The Fact Extraction and VERification (FEVER) Shared Task
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.
Overview of BioCreative II gene mention recognition
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.
Emergent: a novel data-set for stance classification
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.
Fact Checking: Task definition and dataset construction
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.
A stopping criterion for active learning
This work presents a stopping criterion for active learning based on the way instances are selected during uncertainty-based sampling and verifies its applicability in a variety of settings.
UvA-DARE ( Digital Academic Repository ) Overview of BioCreative II gene mention recognition
Nineteen teams presented results for the Gene Mention Task at the BioCreative II Workshop. In this task participants designed systems to identify substrings in sentences corresponding to gene name
Automated Fact Checking: Task Formulations, Methods and Future Directions
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.
Semantic Parsing as Machine Translation
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.