Evidence retrieval is a core part of automatic fact-checking. Prior work
makes simplifying assumptions in retrieval that depart from real-world use
cases: either no access to evidence, access to evidence curated by a human
fact-checker, or access to evidence available long after the claim has been
made. In this work, we present the first fully automated pipeline to check
real-world claims by retrieving raw evidence from the web. We restrict our
retriever to only search documents available prior to the claim's making,
modeling the realistic scenario where an emerging claim needs to be checked.
Our pipeline includes five components: claim decomposition, raw document
retrieval, fine-grained evidence retrieval, claim-focused summarization, and
veracity judgment. We conduct experiments on complex political claims in the
ClaimDecomp dataset and show that the aggregated evidence produced by our
pipeline improves veracity judgments. Human evaluation finds the evidence
summary produced by our system is reliable (it does not hallucinate
information) and relevant to answering key questions about a claim, suggesting
that it can assist fact-checkers even when it cannot surface a complete
evidence set.

本文提出了一种完全自动化的流水线，通过从 Web 检索原始证据来检查实际世界中的索赔，结果表明，我们的流水线可以提供可靠且相关的证据摘要，从而帮助事实检查员甄别真假索赔。