Ensemble learning with soft-prompted pretrained language models for fact checking
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Published:2024-06
Issue:
Volume:7
Page:100067
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ISSN:2949-7191
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Container-title:Natural Language Processing Journal
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language:en
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Short-container-title:Natural Language Processing Journal
Author:
Huang Shaoqin, Wang YueORCID, Wong Eugene Y.C., Yu Lei
Reference49 articles.
1. Afroz, S., Brennan, M., Greenstadt, R., 2012. Detecting hoaxes, frauds, and deception in writing style online. In: Proceedings of 2012 IEEE Symposium on Security and Privacy. pp. 461–475. 2. Alhindi, T., Petridis, S., Muresan, S., 2018. Where is your evidence: Improving fact-checking by justification modeling. In: Proceedings of the First Workshop on Fact Extraction and VERification. FEVER, pp. 85–90. 3. Arana-Catania, M., Kochkina, E., Zubiaga, A., Liakata, M., Procter, R., He, Y., 2022. Natural language inference with self-attention for veracity assessment of pandemic claims. In: Proceedings of NAACL. 4. Enriching word vectors with subword information;Bojanowski,2016 5. Types, Sources, and Claims of COVID-19 Misinformation. Key Findings;Brennen,2020
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