Abstract
This paper presents our work for the Violence Inciting Text Detection shared task in the First Workshop on Bangla Language Processing. Social media has accelerated the propagation of hate and violence-inciting speech in society. It is essential to develop efficient mechanisms to detect and curb the propagation of such texts. The problem of detecting violence-inciting texts is further exacerbated in low-resource settings due to sparse research and less data. The data provided in the shared task consists of texts in the Bangla language, where each example is classified into one of the three categories defined based on the types of violence-inciting texts. We try and evaluate several BERT-based models, and then use an ensemble of the models as our final submission. Our submission is ranked 10th in the final leaderboard of the shared task with a macro F1 score of 0.737.
Abstract (translated)
本文代表我们在第一届孟加拉语言处理研讨会上的工作，研究了如何检测社交媒体上可能引起暴力和仇恨言论的文稿。社交媒体加速了社会中暴力和仇恨言论的传播。在资源有限的环境中，检测和遏制这种文本的传播变得尤为重要。在共享任务中提供的数据中，每篇文章都基于孟加拉语，并根据可能引起暴力和仇恨言论的文稿类型将其归类为三种不同的类别。我们试图评估几种基于BERT的模型，然后将模型的集合作为我们最终的提交。我们提交的论文在共享任务的最终排行榜上排名第10，并具有微宏F1分数为0.737。
URL
https://arxiv.org/abs/2311.18778
