في هذه الورقة، نقدم مساهمة UPAPPLIEDCL في مهمة جيرفال 2021 المشتركة.على وجه الخصوص، شاركنا في SubTasks 2 (تصنيف التعليق التجاري) و 3 (تصنيف التعليق الذي يدعي الحقائق).على الرغم من أن النتائج المقبولة يمكن الحصول عليها باستخدام أجهزة UNIGRAMS أو الميزات اللغوية بالاشتراك مع نماذج تعلم الآلة التقليدية، فإننا نوضح أنه لكلا نماذج محولات المهام المدربة تدريبا على أشرطة Berted Bertdings التي تحمل أفضل نتائج.
In this paper we present UPAppliedCL's contribution to the GermEval 2021 Shared Task. In particular, we participated in Subtasks 2 (Engaging Comment Classification) and 3 (Fact-Claiming Comment Classification). While acceptable results can be obtained by using unigrams or linguistic features in combination with traditional machine learning models, we show that for both tasks transformer models trained on fine-tuned BERT embeddings yield best results.
References used
https://aclanthology.org/
This paper addresses the identification of toxic, engaging, and fact-claiming comments on social media. We used the dataset made available by the organizers of the GermEval2021 shared task containing over 3,000 manually annotated Facebook comments in
We present the GermEval 2021 shared task on the identification of toxic, engaging, and fact-claiming comments. This shared task comprises three binary classification subtasks with the goal to identify: toxic comments, engaging comments, and comments
The availability of language representations learned by large pretrained neural network models (such as BERT and ELECTRA) has led to improvements in many downstream Natural Language Processing tasks in recent years. Pretrained models usually differ i
In this paper, we report on our approach to addressing the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments for the German language. We submitted three runs for each subtask based on ensembles of three mo
This paper describes our methods submitted for the GermEval 2021 shared task on identifying toxic, engaging and fact-claiming comments in social media texts (Risch et al., 2021). We explore simple strategies for semi-automatic generation of rule-base