IITD在WANLP 2022共享任务:用于宣传检测的多语言多粒度网络

Shubham Mittal, Preslav Nakov
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引用次数: 3

摘要

我们为WANLP ' 2022的一部分阿拉伯语宣传检测共享任务的两个子任务展示了我们的系统。子任务1是一个多标签分类问题,用于找到给定tweet中使用的宣传技术。该任务的系统使用XLM-R来预测目标tweet使用每种技术的概率。除了查找技术之外,子任务2还要求确定tweet中存在的每种技术的每个实例的文本跨度;该任务可以建模为序列标记问题。对于子任务2,我们使用带有mBERT编码器的多粒度网络。总的来说,我们的系统在这两个子任务上排名第二(分别在14个和3个参与者中)。我们的实验结果和分析表明,无论是在英语中使用还是翻译成阿拉伯语后使用,使用更大的带有宣传技术注释的英语语料库都没有帮助。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
IITD at WANLP 2022 Shared Task: Multilingual Multi-Granularity Network for Propaganda Detection
We present our system for the two subtasks of the shared task on propaganda detection in Arabic, part of WANLP’2022. Subtask 1 is a multi-label classification problem to find the propaganda techniques used in a given tweet. Our system for this task uses XLM-R to predict probabilities for the target tweet to use each of the techniques. In addition to finding the techniques, subtask 2 further asks to identify the textual span for each instance of each technique that is present in the tweet; the task can be modelled as a sequence tagging problem. We use a multi-granularity network with mBERT encoder for subtask 2. Overall, our system ranks second for both subtasks (out of 14 and 3 participants, respectively). Our experimental results and analysis show that it does not help to use a much larger English corpus annotated with propaganda techniques, regardless of whether used in English or after translation to Arabic.
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