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Register a new userAn Edge-Enhanced Hierarchical Graph-to-Tree Network for Math Word Problem Solving
شبكة رسم بياني هرمي محسنة على حافة
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اجتذبت حل مشكلة كلمة الرياضيات اهتماما كبيرا بحوثا كبيرا في السنوات الأخيرة. أظهرت الأعمال السابقة فعالية استخدام الشبكات العصبية الرسم البيانية لالتقاط العلاقات في المشكلة. ومع ذلك، فإن هذه الأعمال لم تأخذ بعناية معلومات تسمية الحافة وعلاقة الكلمة الطويلة المدى عبر الجمل في الاعتبار. بالإضافة إلى ذلك، أثناء التوليد، يركزون على أكثر المناطق ذات صلة بالكلمة التي تم إنشاؤها حاليا، مع إهمال بقية المشكلة. في هذه الورقة، نقترح نموذج رسم بياني هرمي ذو طراز ذو طراز هيروسي محسن على حافة على وجه التحديد، يتم استخدام تشفير الرسوم البيانية التسلسل الهرمية المحسنة على الحافة لدمج معلومات تسمية الحافة. يقوم هذا التشفير بتحديث عقود الرسم البياني هرمي خطوتين في خطوتين: تجميع مستوى الجملة ومجموعة على مستوى المشكلات. علاوة على ذلك، يتم تطبيق وحدة فك ترميز منظم من الأشجار مع آلية انحراف تقسيم لتوجيه النموذج للانتباه إلى أجزاء مختلفة من مشكلة الإدخال. أظهرت النتائج التجريبية على المؤشرات ومجموعات البيانات MAWPS و Math23K أن EEH-G2T يمكن أن تحسن بشكل فعال الأداء بشكل فعال مقارنة بالطرق الحديثة.
Math word problem solving has attracted considerable research interest in recent years. Previous works have shown the effectiveness of utilizing graph neural networks to capture the relationships in the problem. However, these works did not carefully take the edge label information and the long-range word relationship across sentences into consideration. In addition, during generation, they focus on the most relevant areas of the currently generated word, while neglecting the rest of the problem. In this paper, we propose a novel Edge-Enhanced Hierarchical Graph-to-Tree model (EEH-G2T), in which the math word problems are represented as edge-labeled graphs. Specifically, an edge-enhanced hierarchical graph encoder is used to incorporate edge label information. This encoder updates the graph nodes hierarchically in two steps: sentence-level aggregation and problem-level aggregation. Furthermore, a tree-structured decoder with a split attention mechanism is applied to guide the model to pay attention to different parts of the input problem. Experimental results on the MAWPS and Math23K dataset showed that our EEH-G2T can effectively improve performance compared with state-of-the-art methods.
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