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Register a new userUniversit\"at Regensburg MaxS at GermEval 2021 Task 1: Synthetic Data in Toxic Comment Classification
Universit \ "في Regensburg Maxs في Germeval 2021 المهمة 1: البيانات الاصطناعية في تصنيف التعليق السام
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نبلغ عن تقديمنا إلى المهمة 1 من تحدي جيرفال 2021 - تصنيف التعليق السام.نحقق في طرق مختلفة لتعزيز البيانات التدريبية النادرة لتحسين الأداء النموذجي خارج الرف على مهمة تصنيف سامة للتعليق.للمساعدة في معالجة قيود مجموعة بيانات صغيرة، نستخدم البيانات التي تم إنشاؤها مزخرف بواسطة نموذج GPT-2 الألماني.إن استخدام البيانات الاصطناعية لم تقلع مؤخرا كحل محتمل لبيانات التدريب التدريجي في مجال التصديق في NLP، والنتائج الأولية تعد.ومع ذلك، لم ير نموذجنا تحسنا قياسيا من خلال استخدام البيانات الاصطناعية.نناقش أسباب محتملة لهذا الاكتشاف واستكشاف الأعمال المستقبلية في هذا المجال.
We report on our submission to Task 1 of the GermEval 2021 challenge -- toxic comment classification. We investigate different ways of bolstering scarce training data to improve off-the-shelf model performance on a toxic comment classification task. To help address the limitations of a small dataset, we use data synthetically generated by a German GPT-2 model. The use of synthetic data has only recently been taking off as a possible solution to ad- dressing training data sparseness in NLP, and initial results are promising. However, our model did not see measurable improvement through the use of synthetic data. We discuss possible reasons for this finding and explore future works in the field.
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In this work, we present our approaches on the toxic comment classification task (subtask 1) of the GermEval 2021 Shared Task. For this binary task, we propose three models: a German BERT transformer model; a multilayer perceptron, which was first tr
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