| 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. |
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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. |
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. |
The Intended Uses of Automated Fact-Checking Artefacts: Why, How and Who (2023.findings-emnlp)
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| Challenge: | Automated fact-checking is often presented as an epistemic tool fact-seekers, social media consumers, and other stakeholders can use to fight misinformation. |
| Approach: | They analyse 100 highly-cited papers and annotate epistemic elements related to intended use, i.e., means, ends, and stakeholders. |
| Outcome: | The proposed strategies are often left out of the literature and lack empirical backing. |
Explainable Automated Fact-Checking for Public Health Claims (2020.emnlp-main)
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| 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. |
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. |
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. |
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. |
COMMUNITYNOTES: A Dataset for Exploring the Helpfulness of Fact-Checking Explanations (2026.findings-eacl)
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| Challenge: | X, Meta, and TikTok are experimenting with community-based factchecking . community-driven verification is a way to provide explanatory notes that clarify why a post might be misleading . |
| Approach: | They propose a framework that optimizes the helpfulness of explanatory notes and the reason for this by automatically optimizing the prompt definitions. |
| Outcome: | The proposed framework improves helpfulness and reason prediction on 104k posts with user-provided notes and helpfulness labels. |
A Survey of the State of Explainable AI for Natural Language Processing (2020.aacl-main)
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| Challenge: | Recent years have seen significant advances in the quality of state-of-the-art models, but they have come at the expense of models becoming less interpretable. |
| Approach: | This survey examines the current state of Explainable AI within the domain of NLP . they detail the operations and explainability techniques currently available for generating explanations for NLP models . |
| Outcome: | This survey examines the state of explainable AI (XAI) within the domain of natural language processing . it focuses on the operations and explainability techniques currently available for NLP models . |
ChartCheck: Explainable Fact-Checking over Real-World Chart Images (2024.findings-acl)
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| Challenge: | Data visualizations are often used to summarize and communicate key information, but they can also be misused to spread misinformation and promote agendas. |
| Approach: | They propose a dataset for explainable fact-checking against real-world charts that uses vision-language and chart-to-table models to evaluate the validity of the dataset. |
| Outcome: | The proposed model is based on vision-language and chart-to-table models and proposes a baseline to the community. |