Complex Claim Verification with Evidence Retrieved in the Wild

• URL: http://arxiv.org/abs/2305.11859v2
• Date: Sat, 15 Jun 2024 06:23:30 GMT
• Title: Complex Claim Verification with Evidence Retrieved in the Wild
• Authors: Jifan Chen, Grace Kim, Aniruddh Sriram, Greg Durrett, Eunsol Choi,
• Abstract summary: We present the first fully automated pipeline to check real-world claims by retrieving raw evidence from the web. Our pipeline includes five components: claim decomposition, raw document retrieval, fine-grained evidence retrieval, claim-focused summarization, and veracity judgment.
• Score: 73.19998942259073
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Contrastive Learning to Improve Retrieval for Real-world Fact Checking [84.57583869042791]
  We present Contrastive Fact-Checking Reranker (CFR), an improved retriever for fact-checking complex claims. We leverage the AVeriTeC dataset, which annotates subquestions for claims with human written answers from evidence documents. We find a 6% improvement in veracity classification accuracy on the dataset.
  arXiv  Detail & Related papers  (2024-10-07T00:09:50Z)
• Search, Examine and Early-Termination: Fake News Detection with Annotation-Free Evidences [32.11238363508177]
  We propose an approach named textbfSEE that retrieves useful information from web-searched annotation-free evidences. The experimental results demonstrate that the proposed method outperforms state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-07-10T07:22:30Z)
• Give Me More Details: Improving Fact-Checking with Latent Retrieval [58.706972228039604]
  Evidence plays a crucial role in automated fact-checking. Existing fact-checking systems either assume the evidence sentences are given or use the search snippets returned by the search engine. We propose to incorporate full text from source documents as evidence and introduce two enriched datasets.
  arXiv  Detail & Related papers  (2023-05-25T15:01:19Z)
• AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web [20.576644330553744]
  We introduce AVeriTeC, a new dataset of 4,568 real-world claims covering fact-checks by 50 different organizations. Each claim is annotated with question-answer pairs supported by evidence available online, as well as textual justifications explaining how the evidence combines to produce a verdict.
  arXiv  Detail & Related papers  (2023-05-22T15:17:18Z)
• Read it Twice: Towards Faithfully Interpretable Fact Verification by Revisiting Evidence [59.81749318292707]
  We propose a fact verification model named ReRead to retrieve evidence and verify claim. The proposed system is able to achieve significant improvements upon best-reported models under different settings.
  arXiv  Detail & Related papers  (2023-05-02T03:23:14Z)
• Generating Literal and Implied Subquestions to Fact-check Complex Claims [64.81832149826035]
  We focus on decomposing a complex claim into a comprehensive set of yes-no subquestions whose answers influence the veracity of the claim. We present ClaimDecomp, a dataset of decompositions for over 1000 claims. We show that these subquestions can help identify relevant evidence to fact-check the full claim and derive the veracity through their answers.
  arXiv  Detail & Related papers  (2022-05-14T00:40:57Z)
• GERE: Generative Evidence Retrieval for Fact Verification [57.78768817972026]
  We propose GERE, the first system that retrieves evidences in a generative fashion. The experimental results on the FEVER dataset show that GERE achieves significant improvements over the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2022-04-12T03:49:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.