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Register a new userA Study on Contextualized Language Modeling for Machine Reading Comprehension
دراسة عن نمذجة اللغة السياقية لتصفية القراءة
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مع الانفراج الأخير لتكنولوجيات التعلم العميق، اجتذبت البحث عن الفهم في قراءة الآلة (MRC) اهتماما كبيرا ووجدت تطبيقاتها متعددة الاستخدامات في العديد من حالات الاستخدام. MRC هي مهمة مهمة لمعالجة اللغة الطبيعية (NLP) تهدف إلى تقييم قدرة الجهاز لفهم تعبيرات اللغة الطبيعية، والتي يتم تشغيلها عادة عن طريق طرح الأسئلة أولا بناء على فقرة نصية معينة ثم تلقي الإجابات التي تم إنشاؤها بواسطة الجهاز وفقا ل إعطاء الفقرة والأسئلة السياق. في هذه الورقة، نستفيد نماذج لغة رواية روبيتين تم بناؤها على رأس تمثيلات التشفير الثنائية من المحولات (بيرت)، وهي بيرت WWM و Macbert، لتطوير أساليب MRC الفعالة. بالإضافة إلى ذلك، نسعى أيضا إلى التحقيق في ما إذا كان قد يستفيد التأسيس الإضافي للمعلومات الفئوية حول فقرة السياق MRC أم لا، والتي تحققت، على أساس تجميع الفقرة السياق على مجموعة بيانات التدريب. من ناحية أخرى، يقترح نهج التعلم الفرعي تسخير القوة التآزرية للنماذج التي تعتمد على برت المذكور أعلاه من أجل زيادة تعزيز أداء MRC.
With the recent breakthrough of deep learning technologies, research on machine reading comprehension (MRC) has attracted much attention and found its versatile applications in many use cases. MRC is an important natural language processing (NLP) task aiming to assess the ability of a machine to understand natural language expressions, which is typically operationalized by first asking questions based on a given text paragraph and then receiving machine-generated answers in accordance with the given context paragraph and questions. In this paper, we leverage two novel pretrained language models built on top of Bidirectional Encoder Representations from Transformers (BERT), namely BERT-wwm and MacBERT, to develop effective MRC methods. In addition, we also seek to investigate whether additional incorporation of the categorical information about a context paragraph can benefit MRC or not, which is achieved based on performing context paragraph clustering on the training dataset. On the other hand, an ensemble learning approach is proposed to harness the synergistic power of the aforementioned two BERT-based models so as to further promote MRC performance.
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