Complex Claim Verification with Evidence Retrieved in the Wild

TL;DR

This paper introduces a fully automated, real-world claim verification system that retrieves and summarizes web evidence available prior to the claim, improving fact-checking accuracy for complex political assertions.

Contribution

It presents the first pipeline capable of verifying claims by retrieving raw, pre-existing web evidence, modeling realistic fact-checking scenarios.

Findings

Evidence aggregation improves veracity judgment accuracy.

System-produced summaries are reliable and relevant.

The pipeline performs well on complex political claims.

Abstract

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.