B angla NLP at BLP -2023 Task 1: Benchmarking different Transformer Models for Violence Inciting Text Detection in B angla (2023)

Abstract: This paper presents the system that we have developed while solving this shared task on violence inciting text detection in Bangla. We explain both the traditional and the recent approaches that we have used to make our models learn. Our proposed system helps to classify if the given text contains any threat. We studied the impact of data augmentation when there is a limited dataset available. Our quantitative results show that finetuning a multilingual-e5-base model performed the best in our task compared to other transformer-based architectures. We obtained a macro F1 of 68.11% in the test set and our performance in this shared task is ranked at 23 in the leaderboard.

## introduction violence inciting text detection (vitd) is the task of identifying text that incites violence in the bangla language. this is a challenging task due to the complexity of the bangla language and the variety of ways in which violence can be incited. the vitd task is important for several reasons. first, it can help to prevent violence by identifying and removing inciting text before it can cause harm. second, it can help to protect people from being targeted by violence. third, it can help to build a more peaceful and tolerant society. there are several challenges to vitd in bangla, and one of them is the scarcity of annotated data, which is due to the limited number of datasets of bangla text that have been annotated for violence. this makes it difficult to train machine learning models that can accurately detect violence inciting text. another challenge is the complexity of the bangla language. bangla is a morphologically rich language, which means that words can have multiple meanings depending on their context. this can make it difficult to identify violence inciting text, as the same words can be used in both violent and non-violent contexts. despite these chal-lenges, there has been some progress in the development of vitd systems for bangla. the vitd task is still a research area, and there is still much work to be done. ## related works sharif et al. (2022) introduced a multilabel dataset in bangla to do aggressive text classification with a hierarchical annotation scheme. (jahan et al., 2022) created a new bangla hate dataset and proposed banglahatebert for abusive language detection in bangla. (romim et al., 2022) introduced a manually labeled large hate speech dataset in bangla. ## system description this section describes our system which is developed to classify violence inciting text written in bangla. this section starts with the shared task description, followed by the description of the dataset released by the shared task organizers, then our proposed architecture which has produced our team's standing on the leaderboard and finally the results achieved and observations made. all the codes and datasets used for performing the experiments are available in https://github.com/ saumajit/banglanlp/tree/main/task_1 . ## conclusion this paper reports the experiments we performed using the transformer-based models to solve this task. we show the impact that data augmentation has while dealing with smaller datasets. future research direction can include exploring recently released large language models to solve similar tasks in a low-resource language like bangla.