Papers with automated fact checking
WatClaimCheck: A new Dataset for Claim Entailment and Inference (2022.acl-long)
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| Challenge: | a dataset for automated fact checking is presented . premise articles are used to verify the veracity of claims . |
| Approach: | They propose a dataset for automated fact checking and an evaluation of state of the art algorithms. |
| Outcome: | The proposed model improves retrieval quality of passages in premise articles . the proposed model predicts claim veracity by inference from premise article . |
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. |
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. |
Embedded Named Entity Recognition using Probing Classifiers (2024.emnlp-main)
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| Challenge: | Streaming text generation requires separate models during inference, which increases computational cost, or destructive fine-tuning of the language model. |
| Approach: | They propose an approach which enables streaming named entity recognition in decoder-only language models without fine-tuning them. |
| Outcome: | The proposed approach maintains high token generation rates with only a negligible decrease in speed of around 1% compared to a baseline of 43.64%. |