In our paper, we present Deep Learning models with a layer differentiated training method which were used for the SHARED TASK@ CONSTRAINT 2021 sub-tasks COVID19 Fake News Detection in English and Hostile Post Detection in Hindi. We propose a Layer Differentiated training procedure for training a pre-trained ULMFiT arXiv:1801.06146 model. We used special tokens to annotate specific parts of the tweets to improve language understanding and gain insights on the model making the tweets more interpretable. The other two submissions included a modified RoBERTa model and a simple Random Forest Classifier. The proposed approach scored a precision and f1 score of 0.96728972 and 0.967324832 respectively for sub-task "COVID19 Fake News Detection in English". Also, Coarse-Grained Hostility f1 Score and Weighted FineGrained f1 score of 0.908648 and 0.533907 respectively for sub-task Hostile Post Detection in Hindi. The proposed approach ranked 61st out of 164 in the sub-task "COVID19 Fake News Detection in English and 18th out of 45 in the sub-task Hostile Post Detection in Hindi".

中文翻译：

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LaDiff ULMFiT：ULMFiT的分层差异训练方法

在我们的论文中，我们介绍了具有分层区分训练方法的深度学习模型，该模型用于SHARED TASK @ CONSTRAINT 2021子任务COVID19英文假新闻检测和印地语敌对后检测。我们提出了一种区分层的训练程序，用于训练预训练的ULMFiT arXiv：1801.06146模型。我们使用特殊标记对推文的特定部分进行注释，以提高语言理解能力并获得有关模型的见解，从而使推文更易于解释。其他两个提交的内容包括修改后的RoBERTa模型和简单的随机森林分类器。对于子任务“英语中的COVID19假新闻检测”，该方法的精确度和f1得分分别为0.96728972和0.967324832。此外，粗粮敌对性f1得分和加权细粮f1得分为0.908648和0。533907分别用于印地语中的子任务敌对后检测。在子任务“英语中的COVID19假新闻检测”中，该方法在164个任务中排名第61，在“印地语”子任务“敌对后检测”中，它在45个任务中排名第18。