يمكن أن يساعد التحقق من المطالبات العلمية الباحثون على العثور بسهولة على الأوراق العلمية المستهدفة مع أدلة الجملة من كوربوس كبيرة للمطالبة المعينة.تقترح بعض الأعمال الموجودة نماذج خطوط الأنابيب على المهام الثلاث من استرجاع مجردة، اختيار الأساس المنطقي والتنبؤ بالموقف.مثل هذه الأعمال لها مشاكل انتشار الأخطاء بين الوحدات النمطية في خط الأنابيب ونقص مشاركة المعلومات القيمة بين الوحدات النمطية.وبالتالي، نقترح نهجا، سميت باسم Arsjoint، والتي تتعلم بالاشتراك الوحدات المهام الثلاثة ذات الإطار الفهم لقراءة الآلة من خلال إدراج معلومات المطالبة.بالإضافة إلى ذلك، نحن نعزز تبادل المعلومات والقيود بين المهام من خلال اقتراح مصطلح تنظيمي بين درجات انتباه الجملة من استرجاع الملخص والمخرجات المقدرة من الاختيار العقلاني.تظهر النتائج التجريبية على DataSet Benchmark Scifact أن نهجنا يتفوق على الأعمال الحالية.
Scientific claim verification can help the researchers to easily find the target scientific papers with the sentence evidence from a large corpus for the given claim. Some existing works propose pipeline models on the three tasks of abstract retrieval, rationale selection and stance prediction. Such works have the problems of error propagation among the modules in the pipeline and lack of sharing valuable information among modules. We thus propose an approach, named as ARSJoint, that jointly learns the modules for the three tasks with a machine reading comprehension framework by including claim information. In addition, we enhance the information exchanges and constraints among tasks by proposing a regularization term between the sentence attention scores of abstract retrieval and the estimated outputs of rational selection. The experimental results on the benchmark dataset SciFact show that our approach outperforms the existing works.
References used
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