يهدف هذا التقرير الفني إلى المهمة المشتركة في Rocling 2021: تحليل المعنويات الأبعاد للنصوص التعليمية.من أجل التنبؤ بالحالات العاطفية للنصوص التعليمية الصينية، نقدم إطارا عمليا من خلال توظيف نماذج اللغة المدربة مسبقا، مثل بيرت و Macbert.يمكن استخلاص العديد من الملاحظات والتحليلات القيمة من سلسلة من التجارب.من النتائج، نجد أن الأساليب المستندة إلى Macbert يمكن أن توفر نتائج أفضل من الأساليب القائمة على BERT على مجموعة التحقق.لذلك، نحن متوسط نتائج التنبؤ بالعديد من النماذج التي تم الحصول عليها باستخدام إعدادات مختلفة كإخراج نهائي.
This technical report aims at the ROCLING 2021 Shared Task: Dimensional Sentiment Analysis for Educational Texts. In order to predict the affective states of Chinese educational texts, we present a practical framework by employing pre-trained language models, such as BERT and MacBERT. Several valuable observations and analyses can be drawn from a series of experiments. From the results, we find that MacBERT-based methods can deliver better results than BERT-based methods on the verification set. Therefore, we average the prediction results of several models obtained using different settings as the final output.
References used
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