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Register a new userNumeracy enhances the Literacy of Language Models
الحساب يعزز معرفة القراءة والكتابة النماذج اللغوية
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أظهرت تمثيلات الأرقام المتخصصة في NLP تحسينات على مهام التفكير العددي مثل مشاكل الكلمات الحسابية والتنبؤ بالرقم المحدد. لكن البشر يستخدمون أيضا الحساب لتحسين الشعور بالمفاهيم العالمية، على سبيل المثال، يمكنك مقعد 5 أشخاص في غرفتك "ولكن ليس 500. هل لديك فهم أفضل للأرقام تحسين فهم النموذج للمفاهيم والكلمات الأخرى؟ تدرس هذه الورقة تأثير استخدام ستة أرقام مختلفة على مهمة التنبؤ بالكلمة الملثمين (MWP)، كوكيل لتقييم معرفة القراءة والكتابة. لدعم هذا التحقيق، نطور Wiki-Convolution، لوحة بيانات 900،000 الجملة المشروحة بأرقام ووحدات، لتجنب حدوث حوادث الأرقام الاسمية والترتيوية. نجد تحسنا كبيرا في MWP للجمل التي تحتوي على أرقام، أن المظلات المتأثرة هي أفضل ترميز أرقام، مما أدى إلى قفزة أكثر من 2 نقطة في دقة التنبؤ عبر خط الأساس بيرت، وأن هذه مهارات محو الأمية المعززة تعميم أيضا إلى السياقات دون أرقام مشروح. نطلق كل الكود في https://git.io/juzxn.
Specialized number representations in NLP have shown improvements on numerical reasoning tasks like arithmetic word problems and masked number prediction. But humans also use numeracy to make better sense of world concepts, e.g., you can seat 5 people in your room' but not 500. Does a better grasp of numbers improve a model's understanding of other concepts and words? This paper studies the effect of using six different number encoders on the task of masked word prediction (MWP), as a proxy for evaluating literacy. To support this investigation, we develop Wiki-Convert, a 900,000 sentence dataset annotated with numbers and units, to avoid conflating nominal and ordinal number occurrences. We find a significant improvement in MWP for sentences containing numbers, that exponent embeddings are the best number encoders, yielding over 2 points jump in prediction accuracy over a BERT baseline, and that these enhanced literacy skills also generalize to contexts without annotated numbers. We release all code at https://git.io/JuZXn.
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