Task-Oriented Automatic Fact-Checking with Frame-Semantics

• URL: http://arxiv.org/abs/2501.13288v2
• Date: Tue, 18 Feb 2025 04:42:08 GMT
• Title: Task-Oriented Automatic Fact-Checking with Frame-Semantics
• Authors: Jacob Devasier, Rishabh Mediratta, Akshith Putta, Chengkai Li,
• Abstract summary: We introduce a pilot dataset of real-world claims extracted from PolitiFact, annotated for large-scale structured data.<n>This dataset underpins two case studies: the first investigates voting-related claims using the Vote semantic frame, while the second explores various semantic frames based on data sources from the Organisation for Economic Co-operation and Development.<n>Our findings demonstrate the effectiveness of frame semantics in improving evidence retrieval and explainability for fact-checking.
• Score: 0.8135825089247968
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel paradigm for automatic fact-checking that leverages frame semantics to enhance the structured understanding of claims and guide the process of fact-checking them. To support this, we introduce a pilot dataset of real-world claims extracted from PolitiFact, specifically annotated for large-scale structured data. This dataset underpins two case studies: the first investigates voting-related claims using the Vote semantic frame, while the second explores various semantic frames based on data sources from the Organisation for Economic Co-operation and Development (OECD). Our findings demonstrate the effectiveness of frame semantics in improving evidence retrieval and explainability for fact-checking. Finally, we conducted a survey of frames evoked in fact-checked claims, identifying high-impact frames to guide future work in this direction.

Related papers

• Multimodal Fact Checking with Unified Visual, Textual, and Contextual Representations [2.139909491081949]
  We propose a unified framework for fine-grained multimodal fact verification called "MultiCheck"<n>Our architecture combines dedicated encoders for text and images with a fusion module that captures cross-modal relationships using element-wise interactions.<n>We evaluate our approach on the Factify 2 dataset, achieving a weighted F1 score of 0.84, substantially outperforming the baseline.
  arXiv  Detail & Related papers  (2025-08-07T07:36:53Z)
• DISRetrieval: Harnessing Discourse Structure for Long Document Retrieval [51.89673002051528]
  DISRetrieval is a novel hierarchical retrieval framework that leverages linguistic discourse structure to enhance long document understanding.<n>Our studies confirm that discourse structure significantly enhances retrieval effectiveness across different document lengths and query types.
  arXiv  Detail & Related papers  (2025-05-26T14:45:12Z)
• Enhancing Frame Detection with Retrieval Augmented Generation [2.5782420501870296]
  We present the first RAG-based approach for frame detection called RCIF (Retrieve Candidates and Identify Frames) Our results show that our retrieval component significantly reduces the complexity of the task by narrowing the search space. Our approach achieves state-of-the-art performance on FrameNet 1.5 and 1.7, demonstrating its robustness in scenarios where only raw text is provided.
  arXiv  Detail & Related papers  (2025-02-17T02:34:02Z)
• FS-RAG: A Frame Semantics Based Approach for Improved Factual Accuracy in Large Language Models [2.1484130681985047]
  We present a novel extension to Retrieval Augmented Generation with the goal of mitigating factual inaccuracies in the output of large language models. Our method draws on the cognitive linguistic theory of frame semantics for the indexing and retrieval of factual information relevant to helping large language models answer queries.
  arXiv  Detail & Related papers  (2024-06-23T17:18:19Z)
• AFaCTA: Assisting the Annotation of Factual Claim Detection with Reliable LLM Annotators [38.523194864405326]
  AFaCTA is a novel framework that assists in the annotation of factual claims. AFaCTA calibrates its annotation confidence with consistency along three predefined reasoning paths. Our analyses also result in PoliClaim, a comprehensive claim detection dataset spanning diverse political topics.
  arXiv  Detail & Related papers  (2024-02-16T20:59:57Z)
• 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)
• Interpretable Automatic Fine-grained Inconsistency Detection in Text Summarization [56.94741578760294]
  We propose the task of fine-grained inconsistency detection, the goal of which is to predict the fine-grained types of factual errors in a summary. Motivated by how humans inspect factual inconsistency in summaries, we propose an interpretable fine-grained inconsistency detection model, FineGrainFact.
  arXiv  Detail & Related papers  (2023-05-23T22:11:47Z)
• WiCE: Real-World Entailment for Claims in Wikipedia [63.234352061821625]
  We propose WiCE, a new fine-grained textual entailment dataset built on natural claim and evidence pairs extracted from Wikipedia. In addition to standard claim-level entailment, WiCE provides entailment judgments over sub-sentence units of the claim. We show that real claims in our dataset involve challenging verification and retrieval problems that existing models fail to address.
  arXiv  Detail & Related papers  (2023-03-02T17:45:32Z)
• Benchmarking the Robustness of LiDAR Semantic Segmentation Models [78.6597530416523]
  In this paper, we aim to comprehensively analyze the robustness of LiDAR semantic segmentation models under various corruptions. We propose a new benchmark called SemanticKITTI-C, which features 16 out-of-domain LiDAR corruptions in three groups, namely adverse weather, measurement noise and cross-device discrepancy. We design a robust LiDAR segmentation model (RLSeg) which greatly boosts the robustness with simple but effective modifications.
  arXiv  Detail & Related papers  (2023-01-03T06:47:31Z)
• 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)
• AmbiFC: Fact-Checking Ambiguous Claims with Evidence [57.7091560922174]
  We present AmbiFC, a fact-checking dataset with 10k claims derived from real-world information needs. We analyze disagreements arising from ambiguity when comparing claims against evidence in AmbiFC. We develop models for predicting veracity handling this ambiguity via soft labels.
  arXiv  Detail & Related papers  (2021-04-01T17:40:08Z)
• DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checking [46.13738685855884]
  We show that current systems for fact-checking are vulnerable to three categories of realistic challenges for fact-checking. We present a system designed to be resilient to these "attacks" using multiple pointer networks for document selection. We find that in handling these attacks we obtain state-of-the-art results on FEVER, largely due to improved evidence retrieval.
  arXiv  Detail & Related papers  (2020-04-27T15:18:49Z)
• Generating Fact Checking Explanations [52.879658637466605]
  A crucial piece of the puzzle that is still missing is to understand how to automate the most elaborate part of the process. This paper provides the first study of how these explanations can be generated automatically based on available claim context. Our results indicate that optimising both objectives at the same time, rather than training them separately, improves the performance of a fact checking system.
  arXiv  Detail & Related papers  (2020-04-13T05:23:25Z)

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.