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Register a new userMapping probability word problems to executable representations
تعيين مشاكل كلمة الاحتمالية للتمثيل القابل للتنفيذ
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في حين أن حل مشاكل كلمة الرياضيات تلقائيا تلقى اهتماما كبيرا في مجتمع NLP، فقد عالجت القليل من الأعمال مشاكل كلمة الاحتمالية على وجه التحديد.في هذه الورقة، نحن نوظف وتحليل النماذج العصبية المختلفة للإجابة على مشاكل هذه الكلمة.في نهج من خطوتين، يتم تعيين نص المشكلة أولا إلى تمثيل رسمي في لغة إعلانية باستخدام نموذج تسلسل إلى تسلسل، ثم يتم تنفيذ التمثيل الناتج باستخدام نظام برمجة احتمالية لتوفير الإجابة.يشتمل طرازنا الأفضل الأداء على تمثيلات الكلمات العامة في مجال العمل العام الذي تم تصويره باستخدام التعلم عبر مجموعة بيانات داخل المجال الأخرى.ونحن نطبق أيضا النماذج الطرفية إلى هذه المهمة، والتي تبرز أهمية النهج من خطوتين في الحصول على حلول صحيحة لمشاكل الاحتمال.
While solving math word problems automatically has received considerable attention in the NLP community, few works have addressed probability word problems specifically. In this paper, we employ and analyse various neural models for answering such word problems. In a two-step approach, the problem text is first mapped to a formal representation in a declarative language using a sequence-to-sequence model, and then the resulting representation is executed using a probabilistic programming system to provide the answer. Our best performing model incorporates general-domain contextualised word representations that were finetuned using transfer learning on another in-domain dataset. We also apply end-to-end models to this task, which bring out the importance of the two-step approach in obtaining correct solutions to probability problems.
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