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Register a new userA Fact Checking and Verification System for FEVEROUS Using a Zero-Shot Learning Approach
فحص الحقائق ونظام التحقق من أجل Fnverous باستخدام نهج التعلم بالرصاص صفر
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في هذه الورقة، نقترح نظام التحقق والتحقق من حقائق جديدة للتحقق من مطالبات محتوى ويكيبيديا.يسترد نظامنا صفحات ويكيبيديا ذات الصلة باستخدام Anserini، ويستخدم نموذج الإجابة على السؤال من Bert-Bert-bert-Berted لتحديد الأدلة الصحيحة، وتحقق من المطالبات باستخدام نموذج الاستدلال باللغة الطبيعية XLNet بمقارنتها بالأدلة.يتم الحصول على أدلة خلية الجدول من خلال البحث عن قيم الخلايا المطابقة للكيان وسؤال الجدول Tapas نموذج الرد على نموذج.يستخدم خط الأنابيب إمكانيات الطلقة الصفرية للنماذج الحالية وجميع النماذج المستخدمة في خط الأنابيب لا يتطلب أي تدريب إضافي.حصل نظامنا على درجة حمامة من 0.06 ودقة ملصقة تبلغ 0.39 في التحدي الحمير.
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, and verifies claims using XLNET natural language inference model by comparing it with the evidence. Table cell evidence is obtained through looking for entity-matching cell values and TAPAS table question answering model. The pipeline utilizes zero-shot capabilities of existing models and all the models used in the pipeline requires no additional training. Our system got a FEVEROUS score of 0.06 and a label accuracy of 0.39 in FEVEROUS challenge.
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