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Register a new userCombining sentence and table evidence to predict veracity of factual claims using TaPaS and RoBERTa
الجمع بين الجملة والأدلة الجدولية للتنبؤ بالكثير من المطالبات الواقعية باستخدام Tapas and Roberta
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تصف هذه الورقة طريقة لاسترداد الأدلة والتنبؤ بعثور على مزاعم واقعية، على مجموعة البيانات المحمولة.تتكون الأدلة من كل من الجمل وخلايا الطاولة.الطريقة المقترحة هي جزء من المهمة المشتركة للحمى.يستخدم درجات التشابه بين متجهات TF-IDF لاسترداد الأدلة النصية ودرجات التشابه بين ناقلات كثيفة التي تم إنشاؤها بواسطة نماذج Tapas التي تم ضبطها بشكل جيد لاسترجاع الأدلة الجدولية.يتم تمرير الأدلة من خلال شبكة عصبية كثيفة لإنتاج تسمية صحية.النتيجة الحميرة للنظام المقترح 0.126.
This paper describes a method for retrieving evidence and predicting the veracity of factual claims, on the FEVEROUS dataset. The evidence consists of both sentences and table cells. The proposed method is part of the FEVER shared task. It uses similarity scores between TF-IDF vectors to retrieve the textual evidence and similarity scores between dense vectors created by fine-tuned TaPaS models for tabular evidence retrieval. The evidence is passed through a dense neural network to produce a veracity label. The FEVEROUS score for the proposed system is 0.126.
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Tables are widely used in various kinds of documents to present information concisely. Understanding tables is a challenging problem that requires an understanding of language and table structure, along with numerical and logical reasoning. In this p
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