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Register a new userAEDA: An Easier Data Augmentation Technique for Text Classification
AEDA: تقنية تكبير البيانات أسهل لتصنيف النص
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تقترح هذه الورقة AEDA (أداة تكبير البيانات أسهل) للمساعدة في تحسين الأداء في مهام تصنيف النص.يتضمن AEDA إدراج عشوائي فقط من علامات الترقيم في النص الأصلي.هذه تقنية أسهل لتنفيذ تكبير البيانات من طريقة EDA (Wei و Zou، 2019) مقارنة نتائجنا.بالإضافة إلى ذلك، فإنه يحتفظ بترتيب الكلمات أثناء تغيير مواقعهم في الجملة المؤدية إلى أداء أفضل معمم.علاوة على ذلك، فإن عملية الحذف في إيدا يمكن أن تسبب فقدان المعلومات التي، بدورها تضلل الشبكة، في حين أن Aeda يحافظ على جميع معلومات الإدخال.بعد خط الأساس، نقوم بإجراء تجارب على خمسة مجموعات بيانات مختلفة لتصنيف النص.نظهر باستخدام البيانات المعززة AEDA للتدريب، تظهر النماذج أداء فائقا مقارنة باستخدام البيانات المعززة إيدا في جميع مجموعات البيانات الخمسة.سيتم توفير شفرة المصدر لمزيد من الدراسة واستنساخ النتائج.
This paper proposes AEDA (An Easier Data Augmentation) technique to help improve the performance on text classification tasks. AEDA includes only random insertion of punctuation marks into the original text. This is an easier technique to implement for data augmentation than EDA method (Wei and Zou, 2019) with which we compare our results. In addition, it keeps the order of the words while changing their positions in the sentence leading to a better generalized performance. Furthermore, the deletion operation in EDA can cause loss of information which, in turn, misleads the network, whereas AEDA preserves all the input information. Following the baseline, we perform experiments on five different datasets for text classification. We show that using the AEDA-augmented data for training, the models show superior performance compared to using the EDA-augmented data in all five datasets. The source code will be made available for further study and reproduction of the results.
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