Many tasks such as question answering and reading comprehension rely on information extracted from unreliable sources. These systems would thus benefit from knowing whether a statement from an unreliable source is correct. We present experiments on the FEVER (Fact Extraction and VERification) task, a shared task that involves selecting sentences from Wikipedia and predicting whether a claim is supported by those sentences, refuted, or there is not enough information. Fact checking is a task that benefits from not only asserting or disputing the veracity of a claim but also finding evidence for that position. As these tasks are dependent on each other, an ideal model would consider the veracity of the claim when finding evidence and also find only the evidence that is relevant. We thus jointly model sentence extraction and verification on the FEVER shared task. Among all participants, we ranked 5th on the blind test set (prior to any additional human evaluation of the evidence).
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