يصف هذا العمل تكيف نموذج تسلسل متطلب مسبقا بمهمة التحقق من المطالبة العلمية في المجال الطبي الطبيعي.نقترح نظام يسمى Vert5erini الذي يستغل T5 لاسترجاع الملخص واختيار الجملة وتنبؤ التسمية، وهي ثلاثة مهام فرعية حرجة للتحقق من الادعاء.نقوم بتقييم خط أنابيبنا في SCIFACT، وهي مجموعة بيانات مفيدة حديثا تتطلب نماذج لا تتوقع فقط عن صحة المطالبات ولكنها توفر أيضا جمل ذات صلة من كائن من الأدبيات العلمية التي تدعم التنبؤ.تجريبيا، يتفوق نظامنا على خط أساس قوي في كل من المهام الفرعية الثلاث.نعرض أيضا قدرة Vert5erini على التعميم لمجموعات بيانات جديدة من مطالبات CovID-19 باستخدام أدلة من Cord-19 Corpus.
This work describes the adaptation of a pretrained sequence-to-sequence model to the task of scientific claim verification in the biomedical domain. We propose a system called VerT5erini that exploits T5 for abstract retrieval, sentence selection, and label prediction, which are three critical sub-tasks of claim verification. We evaluate our pipeline on SciFACT, a newly curated dataset that requires models to not just predict the veracity of claims but also provide relevant sentences from a corpus of scientific literature that support the prediction. Empirically, our system outperforms a strong baseline in each of the three sub-tasks. We further show VerT5erini's ability to generalize to two new datasets of COVID-19 claims using evidence from the CORD-19 corpus.
References used
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