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Register a new userMining Commonsense and Domain Knowledge from Math Word Problems
التعدين المنطقي ومعرفة المجال من مشاكل كلمة الرياضيات
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تعرف حلال الرياضيات العصبي الحالي دمج المعرفة المنطقية أو المجال عن طريق الاستفادة من الثوابت أو الصيغ المحددة مسبقا.ومع ذلك، نظرا لأن هذه الثوابت والصيغ هي أساسا، فإن تعميمات الحلول محدودة.في هذه الورقة، نقترح استعادة المعرفة المطلوبة صراحة من مشكلة الرياضيات.وبهذه الطريقة، يمكننا مصممة معرفة المعرفة المطلوبة Andimprove شرح الحلول.خوارمنا لدينا تأخذ مشكلة النص ومعادلات الحل كمدخل.ثم، يحاولون استنتاج المعرفة المنطقية والمجال المطلوبة عن طريق دمج المعلومات من كلا الجزأين.نبني اثنين من مجموعات بيانات الرياضيات وتظهر فعالية خوارزمياتنا التي يمكنهم استرداد المعرفة المطلوبة لحل المشكلات.
Current neural math solvers learn to incorporate commonsense or domain knowledge by utilizing pre-specified constants or formulas. However, as these constants and formulas are mainly human-specified, the generalizability of the solvers is limited. In this paper, we propose to explicitly retrieve the required knowledge from math problemdatasets. In this way, we can determinedly characterize the required knowledge andimprove the explainability of solvers. Our two algorithms take the problem text andthe solution equations as input. Then, they try to deduce the required commonsense and domain knowledge by integrating information from both parts. We construct two math datasets and show the effectiveness of our algorithms that they can retrieve the required knowledge for problem-solving.
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