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أذكر وتعلم: حلال مخصص للذاكرة لمشاكل كلمة الرياضيات
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في هذه المقالة، نتعامل مع مشكلة كلمة الرياضيات، وهي الإجابة تلقائيا على مشكلة رياضية وفقا لوصفها النصي. على الرغم من أن الطرق الحديثة أظهرت نتائجها الواعدة، فإن معظم هذه الطرق تستند إلى مخطط الجيل القائم على القوالب تؤدي إلى إمكانية تعميم محدودية. تحقيقا لهذه الغاية، نقترح طريقة التعلم التناظرية تشبه الإنسان الرواية في استدعاء وتعلم بطريقة. يتكون إطار عملنا المقترح من وحدات من وحدات الذاكرة والتمثيل والبيان والتفكير، والتي تم تصميمها لإجراء عملية جديدة من خلال الإشارة إلى التدريبات المستفادة في الماضي. على وجه التحديد، بالنظر إلى مشكلة كلمة الرياضيات، يسترجع النموذج لأول مرة أسئلة مماثلة عن طريق وحدة الذاكرة ثم ترميز المشكلة غير المحددة، وكل سؤال استرجاع باستخدام وحدة تمثيل. علاوة على ذلك، فإن حل المشكلة في طريقة التشبيه، وتقترح وحدة تشبيه ووحدة التفكير مع آلية نسخ نموذج العلاقة المتبادلة بين المشكلة وكل سؤال استرجاع. تظهر تجارب واسعة على مجموعة من مجموعات عمليتين معروفتين تفوق خوارزمية لدينا مقارنة بالمقارنة مع المنافسين غير الفنون الآخرين من كل من مقارنة الأداء الإجمالي ودراسات النطاق الصغير.
In this article, we tackle the math word problem, namely, automatically answering a mathematical problem according to its textual description. Although recent methods have demonstrated their promising results, most of these methods are based on template-based generation scheme which results in limited generalization capability. To this end, we propose a novel human-like analogical learning method in a recall and learn manner. Our proposed framework is composed of modules of memory, representation, analogy, and reasoning, which are designed to make a new exercise by referring to the exercises learned in the past. Specifically, given a math word problem, the model first retrieves similar questions by a memory module and then encodes the unsolved problem and each retrieved question using a representation module. Moreover, to solve the problem in a way of analogy, an analogy module and a reasoning module with a copy mechanism are proposed to model the interrelationship between the problem and each retrieved question. Extensive experiments on two well-known datasets show the superiority of our proposed algorithm as compared to other state-of-the-art competitors from both overall performance comparison and micro-scope studies.
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