اكتسب فحص الحقائق الحاسوبية الكثير من الجر في مجتمعات تعلم الآلات ومعالجة اللغة الطبيعية.تم تطوير عدد كبير من الحلول، لكن الأساليب التي تستفيد من كل من المعلومات الهيكلية وغير المنظمة للكشف عن المعلومات الخاطئة ذات أهمية خاصة.في هذه الورقة، نتعامل مع التحدي الحمير (استخراج الحقائق والتحقق من المعلومات غير المنظمة والمعلومات المهيكلة) التي تتكون من نظام أساسي من المصدر مفتوح مع مجموعة بيانات معيار تحتوي على 87،026 مطالبات تم التحقق منها.نقوم بتمديد هذا النموذج الأساسي هذا من خلال تحسين وحدة استرجاع الأدلة التي تسفر عن أفضل دليل F1 بين المنافسين في لوحة المتصدرين التحدي أثناء الحصول على درجة حمامة إجمالية قدرها 0.20 (أفضل نظام في المرتبة الخامسة).
Computational fact-checking has gained a lot of traction in the machine learning and natural language processing communities. A plethora of solutions have been developed, but methods which leverage both structured and unstructured information to detect misinformation are of particular relevance. In this paper, we tackle the FEVEROUS (Fact Extraction and VERification Over Unstructured and Structured information) challenge which consists of an open source baseline system together with a benchmark dataset containing 87,026 verified claims. We extend this baseline model by improving the evidence retrieval module yielding the best evidence F1 score among the competitors in the challenge leaderboard while obtaining an overall FEVEROUS score of 0.20 (5th best ranked system).
References used
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