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اتساع أول منطق الرسم البياني ل
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تم استخدام شبكة الرسم العصبي الرسمية مؤخرا كأداة واعدة في مهمة الإجابة على السؤال المتعدد القفزات. ومع ذلك، فإن التحديثات غير الضرورية والإنشاءات الحافة البسيطة تمنع استخراج سبان إجابة دقيقة بطريقة أكثر مباشرة وتفسيرها. في هذه الورقة، نقترح نموذجا جديدا من الرسم البياني للسباق الأول (BFR-Graph)، والذي يقدم رسالة جديدة تمرير طريقة تتوافق بشكل أفضل مع عملية التفكير. في Bfr-Graph، يجب أن تبدأ رسالة المنطق من العقدة والسؤال إلى الجمل التالية عقدة هوب من القفزة حتى يتم تمرير جميع الحواف، والتي يمكن أن تمنع كل عقدة بشكل فعال من التعويض الزائد أو تحديث عدة مرات غير ضرورية وبعد لإدخال المزيد من الدلالات، نحدد أيضا الرسم البياني للمنطق كشركة بيانية مرجحة مع النظر في عدد كيانات الحدوث والمسافة بين الجمل. ثم نقدم طريقة أكثر مباشرة وتفسيرا لتجميع الدرجات من مستويات مختلفة من الحبيبات القائمة على GNN. على المتصدرين Hotpotqa، يحقق BFR-Graph المقترح على التنبؤ الحديث في الإجابة على التنبؤ.
Recently Graph Neural Network (GNN) has been used as a promising tool in multi-hop question answering task. However, the unnecessary updations and simple edge constructions prevent an accurate answer span extraction in a more direct and interpretable way. In this paper, we propose a novel model of Breadth First Reasoning Graph (BFR-Graph), which presents a new message passing way that better conforms to the reasoning process. In BFR-Graph, the reasoning message is required to start from the question node and pass to the next sentences node hop by hop until all the edges have been passed, which can effectively prevent each node from over-smoothing or being updated multiple times unnecessarily. To introduce more semantics, we also define the reasoning graph as a weighted graph with considering the number of co-occurrence entities and the distance between sentences. Then we present a more direct and interpretable way to aggregate scores from different levels of granularity based on the GNN. On HotpotQA leaderboard, the proposed BFR-Graph achieves state-of-the-art on answer span prediction.
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