WANLP 2022共享任务:使用数据增强和AraBERT预训练模型的阿拉伯语宣传检测

Sahinur Rahman Laskar, Rahul Singh, Abdullah Faiz Ur Rahman Khilji, Riyanka Manna, Partha Pakray, Sivaji Bandyopadhyay
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引用次数: 3

摘要

在当今时代,在线用户经常接触到带有宣传意味的媒体帖子。已经制定了若干战略,以促进更安全的阿拉伯语媒体消费,以对付这种情况。然而,可用的多标签注释社交媒体数据集有限。在这项工作中,我们通过数据增强对扩展的列车数据使用了预训练的AraBERT基于twitter的模型。我们的团队CNLP-NITS-PP在WANLP-2022的阿拉伯语宣传检测(共享任务)子任务1中获得了微f1分数0.602的第三名。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CNLP-NITS-PP at WANLP 2022 Shared Task: Propaganda Detection in Arabic using Data Augmentation and AraBERT Pre-trained Model
In today’s time, online users are regularly exposed to media posts that are propagandistic. Several strategies have been developed to promote safer media consumption in Arabic to combat this. However, there is a limited available multilabel annotated social media dataset. In this work, we have used a pre-trained AraBERT twitter-base model on an expanded train data via data augmentation. Our team CNLP-NITS-PP, has achieved the third rank in subtask 1 at WANLP-2022, for propaganda detection in Arabic (shared task) in terms of micro-F1 score of 0.602.
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