NLP_CUET at BLP-2023 Task 1: Fine-grained Categorization of Violence Inciting Text using Transformer-based Approach
Jawad Hossain, Hasan Mesbaul Ali Taher, Avishek Das, Mohammed Moshiul Hoque · 2023
The amount of online textual content has increased significantly in recent years through social media posts, online chatting, web portals, and other digital platforms due to the significant increase in internet users and their unprompted access via digital devices.Unfortunately, the misappropriation of textual communication via the Internet has led to violenceinciting texts.Despite the availability of various forms of violence-inciting materials, textbased content is often used to carry out violent acts.Thus, developing a system to detect violence-inciting text has become vital.However, creating such a system in a low-resourced language like Bangla becomes challenging.Therefore, a shared task has been arranged to detect violence-inciting text in Bangla.This paper presents a hybrid approach (GAN+Bangla-ELECTRA) to classify violence-inciting text in Bangla into three classes: direct, passive, and non-violence.We investigated a variety of deep learning (CNN, BiLSTM, BiLSTM+Attention), machine learning (LR, DT, MNB, SVM, RF, SGD), transformers (BERT, ELECTRA), and GAN-based models to detect violence inciting text in Bangla.Evaluation results demonstrate that the GAN+Bangla-ELECTRA model gained the highest macro f 1 -score (74.59), which obtained us a rank of 3rd position at the BLP-2023 Task 1.