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Register a new userMath Word Problem Generation with Mathematical Consistency and Problem Context Constraints
توليد مشكلة كلمة الرياضيات مع الاتساق الرياضي وقيود سياق المشكلات
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ندرس مشكلة توليد مشاكل كلمة الرياضيات الحسابية (MWPS) بالنظر إلى معادلة الرياضيات التي تحدد الحساب الرياضي والسياق الذي يحدد سيناريو المشكلة.الأساليب الحالية عرضة لتوليد MWPS والتي هي إما غير صالحة للرياضيات أو لها جودة لغة غير مرضية.كما أنها إما تتجاهل السياق أو تتطلب مواصفات يدوية لقالب مشكلة، والتي تساوم، تنوع mwps التي تم إنشاؤها.في هذه الورقة، نحن نطور نهج جيل MWP الجديد الذي يرفع طرازات اللغة المدربة مسبقا ونموذج اختيار الكلمات الرئيسية السياق لتحسين جودة اللغة من MWPS المولدة والثاني) لقيود اتساق المعادلة لمعادلات الرياضيات لتحسين الصلاحية الرياضيةmwps التي تم إنشاؤها.تجارب كمية واسعة من الكمية والنوعية على ثلاثة مجموعات بيانات MWP العالمية الحقيقية توضح الأداء الفائق لنهجنا مقارنة مع خطوط الأساس المختلفة.
We study the problem of generating arithmetic math word problems (MWPs) given a math equation that specifies the mathematical computation and a context that specifies the problem scenario. Existing approaches are prone to generating MWPs that are either mathematically invalid or have unsatisfactory language quality. They also either ignore the context or require manual specification of a problem template, which compromises the diversity of the generated MWPs. In this paper, we develop a novel MWP generation approach that leverages i) pre-trained language models and a context keyword selection model to improve the language quality of generated MWPs and ii) an equation consistency constraint for math equations to improve the mathematical validity of the generated MWPs. Extensive quantitative and qualitative experiments on three real-world MWP datasets demonstrate the superior performance of our approach compared to various baselines.
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