Papers with HEALTHVER
Multi2Claim: Generating Scientific Claims from Multi-Choice Questions for Scientific Fact-Checking (2023.eacl-main)
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Neset Tan, Trung Nguyen, Josh Bensemann, Alex Peng, Qiming Bao, Yang Chen, Mark Gahegan, Michael Witbrock
| Challenge: | Existing scientific fact-checking datasets are limited due to expertise bottleneck . multi2Claim pipeline is a tool to convert multiple-choice questions into fact- checking data . |
| Approach: | They propose a pipeline for automatically converting multiple-choice questions into fact-checking data . they generate two large-scale datasets for scientific-fact-checker tasks . success at this task can help the reader understand scientific topics and promote science . |
| Outcome: | The proposed pipeline improves performance on two large-scale scientific fact-checking datasets. |
QaDialMoE: Question-answering Dialogue based Fact Verification with Mixture of Experts (2022.findings-emnlp)
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| Challenge: | Existing research on fact verification focuses on news, tables and Wikipedia passages. |
| Approach: | They propose a question-answering dialogue based fact verification with mixture of experts that exploits questions and evidence effectively in the verification process. |
| Outcome: | The proposed approach outperforms previous approaches on three benchmark datasets and achieves state-of-the-art results. |
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. |
LI4: Label-Infused Iterative Information Interacting Based Fact Verification in Question-answering Dialogue (2024.lrec-main)
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| Challenge: | Existing studies on fact verification have failed to fully exploit question structures and ignoring relevant label information during the verification process. |
| Approach: | They propose a new approach for question-answering dialogue based fact verification using label-infused iterative information interacting. |
| Outcome: | The proposed approach achieves remarkable performance on HEALTHVER, FAVIQ, and COLLOQUIAL. |