Graph-based Retrieval for Claim Verification over Cross-Document Evidence

• URL: http://arxiv.org/abs/2109.06022v1
• Date: Mon, 13 Sep 2021 14:54:26 GMT
• Title: Graph-based Retrieval for Claim Verification over Cross-Document Evidence
• Authors: Misael Mongiov\`i and Aldo Gangemi
• Abstract summary: We conjecture that a graph-based approach can be beneficial to identify fragmented evidence. We tested this hypothesis by building, over the whole corpus, a large graph that interconnects text portions by means of mentioned entities. Our experiments show that leveraging on a graph structure is beneficial in identifying a reasonably small portion of passages related to a claim.
• Score: 0.6853165736531939
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Verifying the veracity of claims requires reasoning over a large knowledge base, often in the form of corpora of trustworthy sources. A common approach consists in retrieving short portions of relevant text from the reference documents and giving them as input to a natural language inference module that determines whether the claim can be inferred or contradicted from them. This approach, however, struggles when multiple pieces of evidence need to be collected and combined from different documents, since the single documents are often barely related to the target claim and hence they are left out by the retrieval module. We conjecture that a graph-based approach can be beneficial to identify fragmented evidence. We tested this hypothesis by building, over the whole corpus, a large graph that interconnects text portions by means of mentioned entities and exploiting such a graph for identifying candidate sets of evidence from multiple sources. Our experiments show that leveraging on a graph structure is beneficial in identifying a reasonably small portion of passages related to a claim.

Related papers

• Unified Multi-Modal Interleaved Document Representation for Information Retrieval [57.65409208879344]
  We produce more comprehensive and nuanced document representations by holistically embedding documents interleaved with different modalities. Specifically, we achieve this by leveraging the capability of recent vision-language models that enable the processing and integration of text, images, and tables into a unified format and representation.
  arXiv  Detail & Related papers  (2024-10-03T17:49:09Z)
• FarFetched: Entity-centric Reasoning and Claim Validation for the Greek Language based on Textually Represented Environments [0.3874856507026475]
  We address the need for automated claim validation based on the aggregated evidence derived from multiple online news sources. We introduce an entity-centric reasoning framework in which latent connections between events, actions, or statements are revealed. Our approach tries to fill the gap in automated claim validation for less-resourced languages.
  arXiv  Detail & Related papers  (2024-07-13T13:30:20Z)
• Heterogeneous Graph Reasoning for Fact Checking over Texts and Tables [22.18384189336634]
  HeterFC is a word-level Heterogeneous-graph-based model for Fact Checking over unstructured and structured information. We perform information propagation via a relational graph neural network, interactions between claims and evidence. We introduce a multitask loss function to account for potential inaccuracies in evidence retrieval.
  arXiv  Detail & Related papers  (2024-02-20T14:10:40Z)
• 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)
• 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)
• Document-Level Relation Extraction with Sentences Importance Estimation and Focusing [52.069206266557266]
  Document-level relation extraction (DocRE) aims to determine the relation between two entities from a document of multiple sentences. We propose a Sentence Estimation and Focusing (SIEF) framework for DocRE, where we design a sentence importance score and a sentence focusing loss. Experimental results on two domains show that our SIEF not only improves overall performance, but also makes DocRE models more robust.
  arXiv  Detail & Related papers  (2022-04-27T03:20:07Z)
• 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)
• Eider: Evidence-enhanced Document-level Relation Extraction [56.71004595444816]
  Document-level relation extraction (DocRE) aims at extracting semantic relations among entity pairs in a document. We propose a three-stage evidence-enhanced DocRE framework consisting of joint relation and evidence extraction, evidence-centered relation extraction (RE), and fusion of extraction results.
  arXiv  Detail & Related papers  (2021-06-16T09:43:16Z)
• Few Shot Learning for Information Verification [0.0]
  We aim to verify facts based on evidence selected from a list of articles taken from Wikipedia. In this research, we aim to verify facts based on evidence selected from a list of articles taken from Wikipedia.
  arXiv  Detail & Related papers  (2021-02-22T12:56:12Z)
• Generating Fact Checking Summaries for Web Claims [8.980876474818153]
  We present a neural attention-based approach that learns to establish the correctness of textual claims based on evidence in the form of text documents. We show the efficacy of our approach on datasets concerning political, healthcare, and environmental issues.
  arXiv  Detail & Related papers  (2020-10-16T18:10:47Z)

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.