共享任务:阿拉伯语推文中的宣传检测

Eshrag A. Refaee, Basem H. A. Ahmed, Motaz K. Saad
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引用次数: 2

摘要

Propaganda是指一个有组织的团体或政府为了影响人们的意见而传播的信息或想法,特别是通过不提供所有事实或秘密地只强调一种看待问题的方式。自动检测与宣传相关的语言符号的能力是一项具有挑战性的任务,NLP社区的研究人员最近开始解决这个问题。本文介绍了我们的团队AraBEM参与阿拉伯语推文的宣传检测共享任务。我们的系统利用预训练的BERT模型进行多类二值分类。它以0.602 micro-f1获得了最高分,在子任务-1上排名第三,该子任务-1将宣传技术识别为多标签分类问题,基线为0.079。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AraBEM at WANLP 2022 Shared Task: Propaganda Detection in Arabic Tweets
Propaganda is information or ideas that an organized group or government spreads to influence peopleś opinions, especially by not giving all the facts or secretly emphasizing only one way of looking at the points. The ability to automatically detect propaganda-related linguistic signs is a challenging task that researchers in the NLP community have recently started to address. This paper presents the participation of our team AraBEM in the propaganda detection shared task on Arabic tweets. Our system utilized a pre-trained BERT model to perform multi-class binary classification. It attained the best score at 0.602 micro-f1, ranking third on subtask-1, which identifies the propaganda techniques as a multilabel classification problem with a baseline of 0.079.
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