| Challenge: | a few blind spots exist in the state-of-the-art in fact-checking for political claims. |
| Approach: | They propose to use a dataset of 11.8K claims to explain fact-check labels for claims . they define and evaluate three coherence properties of explanation quality with humans . |
| Outcome: | The proposed model can be trained on in-domain data and evaluates its coherence properties with humans and computationally. |
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Explainable Automated Fact-Checking: A Survey (2020.coling-main)
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| Challenge: | Steady progress has been made in fact-checking and its orthogonal tasks. |
| Approach: | They propose to use fact-checking explanations to explain predictions by comparing existing explanations against desirable properties to find out what makes for good explanations. |
| Outcome: | The proposed explanations are compared against desirable properties and show how they may lead to improvements in the research area. |
Generating Fact Checking Explanations (2020.acl-main)
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| Challenge: | Existing work on automated fact checking is concerned with predicting the veracity of claims based on metadata, social network spread, language used in claims, and, more recently, evidence supporting or denying claims. |
| Approach: | They propose to combine the generation of justifications for verdicts on claims with the multi-task model to optimize both objectives at the same time rather than training them separately. |
| Outcome: | The proposed model improves the informativeness, coverage and overall quality of the generated explanations, rather than training them separately. |
HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checking (2024.lrec-main)
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| Challenge: | determining the trustworthiness of online medical content is challenging in the digital age . fact-checking is an approach to assess the veracity of factual claims . a new dataset is presented to help advance automated fact- checking . |
| Approach: | They propose a dataset that assesses the veracity of factual claims using evidence from credible sources. |
| Outcome: | The proposed dataset can be used for automated fact-checking tasks. |
Automated Justification Production for Claim Veracity in Fact Checking: A Survey on Architectures and Approaches (2024.acl-long)
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| Challenge: | Current research focuses on predicting claim veracity through metadata analysis and language scrutiny, with an emphasis on justifying verdicts. |
| Approach: | They propose a comprehensive taxonomy for categorizing works based on various criteria and propose scalable methodologies for improving fact-checking explainability. |
| Outcome: | The proposed taxonomy identifies challenges while proposing future directions in fact-checking explainability. |
A Survey on Automated Fact-Checking (2022.tacl-1)
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| Challenge: | Fact-checking is an essential task in journalism due to the speed with which information and misinformation can spread in the media ecosystem. |
| Approach: | They propose to use natural language processing to automate fact-checking by identifying common concepts and defining definitions. |
| Outcome: | The proposed method can predict the veracity of claims using natural language processing, machine learning, and databases. |
Evidence-based Fact-Checking of Health-related Claims (2021.findings-emnlp)
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| Challenge: | Existing evidence-based factchecking datasets contain synthetic claims and lack real-world verification. |
| Approach: | They propose a dataset for evidence-based fact-checking of health-related claims that evaluates their truthfulness against scientific articles. |
| Outcome: | The proposed dataset evaluates real-world claims against scientific articles. |
Automated Fact-Checking of Claims from Wikipedia (2020.lrec-1)
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| Challenge: | Fact checking datasets such as FEVER and SNLI suffer from limited applicability due to synthetic nature of claims and/or evidence written by annotators that differ from real claims and evidence on the internet. |
| Approach: | They present a dataset of 124k+ triples consisting of a claim, context and an evidence document extracted from English Wikipedia articles and citations. |
| Outcome: | The proposed dataset is the largest fact checking dataset consisting of real claims and evidence to date. |
Automated Fact Checking: Task Formulations, Methods and Future Directions (C18-1)
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| Challenge: | Recent research on fact checking has focused on misinformation . however, relevant papers and articles have been published in research communities that are unaware of each other and use inconsistent terminology. |
| Approach: | They propose avenues for future NLP research on automated fact checking . they highlight the use of evidence as an important distinguishing factor . |
| Outcome: | The proposed methods unify the task formulations and methodologies across papers and authors. |
PubHealthTab: A Public Health Table-based Dataset for Evidence-based Fact Checking (2022.findings-naacl)
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| Challenge: | Fact-checking is the task of establishing the veracity of factual information, commonly performed manually by journalists. |
| Approach: | They propose a table fact-checking dataset based on real world public health claims and noisy evidence tables from sources similar to those used by fact checkers. |
| Outcome: | The proposed dataset achieves an overall F1 score of 0.73 . |
Scientific Fact-Checking: A Survey of Resources and Approaches (2023.findings-acl)
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| Challenge: | Fact-checking is the task of assessing the veracity of factual claims based on credible evidence and background knowledge. |
| Approach: | They propose to automate scientific fact-checking using natural language processing to assess the veracity of factual claims based on credible evidence and background knowledge. |
| Outcome: | The proposed methods can help combat the spread of misinformation and help individuals understand new scientific breakthroughs. |