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Register a new userExtractive and Abstractive Explanations for Fact-Checking and Evaluation of News
تفسيرات خارجية ومخفية لفحص الحقائق وتقييم الأخبار
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في هذه الورقة، نستكشف بناء تفسيرات لغة طبيعية للحصول على مطالبات الأخبار، بهدف مساعدة تطبيقات التحقق من الحقائق وتقييم الأخبار.نقوم بتجربة طريقتين: (1) طريقة استخراجية تستند إلى Textrank متحيز - خوارزمية فعالة من الموارد القائمة على الرسم البياني لاستخراج المحتوى؛و (2) طريقة إخراج بناء على نموذج لغة GPT-2.نحن نقوم بإجراء تقييمات مقارنة على مجموعة من مجموعات البيانات الخاطئة في مجالات الأخبار السياسية والصحية، وتجد أن الطريقة الاستخراجية تظهر أكثر الوعد.
In this paper, we explore the construction of natural language explanations for news claims, with the goal of assisting fact-checking and news evaluation applications. We experiment with two methods: (1) an extractive method based on Biased TextRank -- a resource-effective unsupervised graph-based algorithm for content extraction; and (2) an abstractive method based on the GPT-2 language model. We perform comparative evaluations on two misinformation datasets in the political and health news domains, and find that the extractive method shows the most promise.
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Automated fact-checking on a large-scale is a challenging task that has not been studied systematically until recently. Large noisy document collections like the web or news articles make the task more difficult. We describe a three-stage automated f
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The task of verifying the truthfulness of claims in textual documents, or fact-checking, has received significant attention in recent years. Many existing evidence-based factchecking datasets contain synthetic claims and the models trained on these d
ata might not be able to verify real-world claims. Particularly few studies addressed evidence-based fact-checking of health-related claims that require medical expertise or evidence from the scientific literature. In this paper, we introduce HEALTHVER, a new dataset for evidence-based fact-checking of health-related claims that allows to study the validity of real-world claims by evaluating their truthfulness against scientific articles. Using a three-step data creation method, we first retrieved real-world claims from snippets returned by a search engine for questions about COVID-19. Then we automatically retrieved and re-ranked relevant scientific papers using a T5 relevance-based model. Finally, the relations between each evidence statement and the associated claim were manually annotated as SUPPORT, REFUTE and NEUTRAL. To validate the created dataset of 14,330 evidence-claim pairs, we developed baseline models based on pretrained language models. Our experiments showed that training deep learning models on real-world medical claims greatly improves performance compared to models trained on synthetic and open-domain claims. Our results and manual analysis suggest that HEALTHVER provides a realistic and challenging dataset for future efforts on evidence-based fact-checking of health-related claims. The dataset, source code, and a leaderboard are available at https://github.com/sarrouti/healthver.
In this paper, we propose a novel fact checking and verification system to check claims against Wikipedia content. Our system retrieves relevant Wikipedia pages using Anserini, uses BERT-large-cased question answering model to select correct evidence
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