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Graphmr: الرسم البياني الشبكة العصبية للانطق الرياضي
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تهدف المنطق الرياضي إلى استنتاج الحلول الراضية بناء على أسئلة الرياضيات المعينة. أثبتت أبحاث معالجة اللغة الطبيعية السابقة فعالية التسلسل إلى التسلسل (SEQ2SEQ) أو المتغيرات ذات الصلة على حل الرياضيات. ومع ذلك، تمكن عدد قليل من الأعمال من استكشاف المعلومات الهيكلية أو النحوية المخفية في التعبيرات (على سبيل المثال، الأسبقية والزملاء). وضعت هذه الرسالة للتحقيق في فائدة مثل هذه المعلومات غير المستغلة للهندسة العصبية. أولا، يتم تمثيل الأسئلة الرياضية بتنسيق الرسوم البيانية داخل تحليل بناء الجملة. تتيح لهم الطبيعة المنظمة للرسوم البيانية أن تمثل علاقات المتغيرات أو المشغلين مع الحفاظ على دلالات التعبيرات. بعد أن تحولت إلى التمثيلات الجديدة، اقترحنا Graphmr الشبكة العصبية الرسمية الرسمية، والتي يمكن أن تتعلم بفعالية المعلومات الهرمية من مدخلات الرسوم البيانية لحل الرياضيات وتوقع الإجابات. يتم بناء سيناريو تجريبي كامل مع أربع فئات من المهام الرياضية وثلاثة خطوط خطوط خطوط خطوط SEQ2SEQ لإجراء تحليل شامل، وتظهر النتائج أن Graphmr تفوقت على الآخرين في التعلم والمعلومات المخفية والحل الرياضيات.
Mathematical reasoning aims to infer satisfiable solutions based on the given mathematics questions. Previous natural language processing researches have proven the effectiveness of sequence-to-sequence (Seq2Seq) or related variants on mathematics solving. However, few works have been able to explore structural or syntactic information hidden in expressions (e.g., precedence and associativity). This dissertation set out to investigate the usefulness of such untapped information for neural architectures. Firstly, mathematical questions are represented in the format of graphs within syntax analysis. The structured nature of graphs allows them to represent relations of variables or operators while preserving the semantics of the expressions. Having transformed to the new representations, we proposed a graph-to-sequence neural network GraphMR, which can effectively learn the hierarchical information of graphs inputs to solve mathematics and speculate answers. A complete experimental scenario with four classes of mathematical tasks and three Seq2Seq baselines is built to conduct a comprehensive analysis, and results show that GraphMR outperforms others in hidden information learning and mathematics resolving.
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