  Claim verification is the task of predicting the veracity of written
statements against evidence. Previous large-scale datasets model the task as
classification, ignoring the need to retrieve evidence, or are constructed for
research purposes, and may not be representative of real-world needs. In this
paper, we introduce a novel claim verification dataset with instances derived
from search-engine queries, yielding 10,987 claims annotated with evidence that
represent real-world information needs. For each claim, we annotate evidence
from full Wikipedia articles with both section and sentence-level granularity.
Our annotation allows comparison between two complementary approaches to
verification: stance classification, and evidence extraction followed by
entailment recognition. In our comprehensive evaluation, we find no significant
difference in accuracy between these two approaches. This enables systems to
use evidence extraction to summarize a rationale for an end-user while
maintaining the accuracy when predicting a claim's veracity. With challenging
claims and evidence documents containing hundreds of sentences, our dataset
presents interesting challenges that are not captured in previous work --
evidenced through transfer learning experiments. We release code and data to
support further research on this task.
