@inproceedings{ahmed-etal-2023-score,
title = "{S}core{\_}{I}s{A}ll{\_}{Y}ou{\_}{N}eed at {BLP}-2023 Task 1: A Hierarchical Classification Approach to Detect Violence Inciting Text using Transformers",
author = "Ahmed, Kawsar and
Osama, Md and
Islam, Md. Sirajul and
Islam, Md Taosiful and
Das, Avishek and
Hoque, Mohammed Moshiul",
editor = "Alam, Firoj and
Kar, Sudipta and
Chowdhury, Shammur Absar and
Sadeque, Farig and
Amin, Ruhul",
booktitle = "Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.banglalp-1.21",
doi = "10.18653/v1/2023.banglalp-1.21",
pages = "185--189",
abstract = "Violence-inciting text detection has become critical due to its significance in social media monitoring, online security, and the prevention of violent content. Developing an automatic text classification model for identifying violence in languages with limited resources, like Bangla, poses significant challenges due to the scarcity of resources and complex morphological structures. This work presents a transformer-based method that can classify Bangla texts into three violence classes: direct, passive, and non-violence. We leveraged transformer models, including BanglaBERT, XLM-R, and m-BERT, to develop a hierarchical classification model for the downstream task. In the first step, the BanglaBERT is employed to identify the presence of violence in the text. In the next step, the model classifies stem texts that incite violence as either direct or passive. The developed system scored 72.37 and ranked 14th among the participants.",
}
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<abstract>Violence-inciting text detection has become critical due to its significance in social media monitoring, online security, and the prevention of violent content. Developing an automatic text classification model for identifying violence in languages with limited resources, like Bangla, poses significant challenges due to the scarcity of resources and complex morphological structures. This work presents a transformer-based method that can classify Bangla texts into three violence classes: direct, passive, and non-violence. We leveraged transformer models, including BanglaBERT, XLM-R, and m-BERT, to develop a hierarchical classification model for the downstream task. In the first step, the BanglaBERT is employed to identify the presence of violence in the text. In the next step, the model classifies stem texts that incite violence as either direct or passive. The developed system scored 72.37 and ranked 14th among the participants.</abstract>
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%0 Conference Proceedings
%T Score_IsAll_You_Need at BLP-2023 Task 1: A Hierarchical Classification Approach to Detect Violence Inciting Text using Transformers
%A Ahmed, Kawsar
%A Osama, Md
%A Islam, Md. Sirajul
%A Islam, Md Taosiful
%A Das, Avishek
%A Hoque, Mohammed Moshiul
%Y Alam, Firoj
%Y Kar, Sudipta
%Y Chowdhury, Shammur Absar
%Y Sadeque, Farig
%Y Amin, Ruhul
%S Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F ahmed-etal-2023-score
%X Violence-inciting text detection has become critical due to its significance in social media monitoring, online security, and the prevention of violent content. Developing an automatic text classification model for identifying violence in languages with limited resources, like Bangla, poses significant challenges due to the scarcity of resources and complex morphological structures. This work presents a transformer-based method that can classify Bangla texts into three violence classes: direct, passive, and non-violence. We leveraged transformer models, including BanglaBERT, XLM-R, and m-BERT, to develop a hierarchical classification model for the downstream task. In the first step, the BanglaBERT is employed to identify the presence of violence in the text. In the next step, the model classifies stem texts that incite violence as either direct or passive. The developed system scored 72.37 and ranked 14th among the participants.
%R 10.18653/v1/2023.banglalp-1.21
%U https://aclanthology.org/2023.banglalp-1.21
%U https://doi.org/10.18653/v1/2023.banglalp-1.21
%P 185-189
Markdown (Informal)
[Score_IsAll_You_Need at BLP-2023 Task 1: A Hierarchical Classification Approach to Detect Violence Inciting Text using Transformers](https://aclanthology.org/2023.banglalp-1.21) (Ahmed et al., BanglaLP 2023)
ACL