| 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. |
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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. |
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. |
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. |
Benchmarking the Generation of Fact Checking Explanations (2023.tacl-1)
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| Challenge: | Automating fact-checking is a time-consuming task that cannot keep up with the ever-increasing amount of fake news produced daily. |
| Approach: | They propose to automate the process of fact-checking by generating justifications from textual explanations of why a claim is classified as either true or false. |
| Outcome: | The proposed approach improves summarization performance over unstructured knowledge and with two datasets with different styles and structures. |
Evaluating Evidence Attribution in Generated Fact Checking Explanations (2025.naacl-long)
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| Challenge: | Existing fact-checking systems struggle with attribution quality, as their generated explanations can include hallucinations. |
| Approach: | They propose a protocol to assess attribution quality in fact-checking explanations using human annotation and automatic annotation. |
| Outcome: | The proposed protocol can be automated, the authors show . best-performing LLMs still generate explanations that are not always accurate . |
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 Role of Context in Detecting Previously Fact-Checked Claims (2022.findings-naacl)
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| Challenge: | Recent years have seen the proliferation of disinformation and fake news online. |
| Approach: | They propose to model the context of a political debate and the contexts of the document describing the fact-checked claim. |
| Outcome: | The proposed model improves on the state-of-the-art model by modeling the context of the claim . the experimental results show that the model can provide 10+ points of improvement over the state of the art model . |
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. |
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. |
Towards a Framework for Evaluating Explanations in Automated Fact Verification (2024.lrec-main)
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| Challenge: | A growing interest has emerged in rationalizing explanations to provide short and coherent justifications for predictions. |
| Approach: | They propose a formal framework for rationalizing explanations to support their evaluation systematically. |
| Outcome: | The proposed framework is tailored to rationalizing explanations of increasingly complex structures, from free-form explanations to argumentative explanations with the richest structure. |