@inproceedings{shibu-etal-2023-sust,
title = "{SUST}{\_}{B}lack Box at {BLP}-2023 Task 1: Detecting Communal Violence in Texts: An Exploration of {MLM} and Weighted Ensemble Techniques",
author = "Shibu, Hrithik and
Datta, Shrestha and
Rahman, Zhalok and
Sami, Shahrab and
Miah, Md. Sumon and
Fairooz, Raisa and
Mollah, Md",
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-1.25",
doi = "10.18653/v1/2023.banglalp-1.25",
pages = "208--213",
abstract = "In this study, we address the shared task of classifying violence-inciting texts from YouTube comments related to violent incidents in the Bengal region. We seamlessly integrated domain adaptation techniques by meticulously fine-tuning pre-existing Masked Language Models on a diverse array of informal texts. We employed a multifaceted approach, leveraging Transfer Learning, Stacking, and Ensemble techniques to enhance our model{'}s performance. Our integrated system, amalgamating the refined BanglaBERT model through MLM and our Weighted Ensemble approach, showcased superior efficacy, achieving macro F1 scores of 71{\%} and 72{\%}, respectively, while the MLM approach secured the 18th position among participants. This underscores the robustness and precision of our proposed paradigm in the nuanced detection and categorization of violent narratives within digital realms.",
}
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<abstract>In this study, we address the shared task of classifying violence-inciting texts from YouTube comments related to violent incidents in the Bengal region. We seamlessly integrated domain adaptation techniques by meticulously fine-tuning pre-existing Masked Language Models on a diverse array of informal texts. We employed a multifaceted approach, leveraging Transfer Learning, Stacking, and Ensemble techniques to enhance our model’s performance. Our integrated system, amalgamating the refined BanglaBERT model through MLM and our Weighted Ensemble approach, showcased superior efficacy, achieving macro F1 scores of 71% and 72%, respectively, while the MLM approach secured the 18th position among participants. This underscores the robustness and precision of our proposed paradigm in the nuanced detection and categorization of violent narratives within digital realms.</abstract>
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%0 Conference Proceedings
%T SUST_Black Box at BLP-2023 Task 1: Detecting Communal Violence in Texts: An Exploration of MLM and Weighted Ensemble Techniques
%A Shibu, Hrithik
%A Datta, Shrestha
%A Rahman, Zhalok
%A Sami, Shahrab
%A Miah, Md. Sumon
%A Fairooz, Raisa
%A Mollah, Md
%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 shibu-etal-2023-sust
%X In this study, we address the shared task of classifying violence-inciting texts from YouTube comments related to violent incidents in the Bengal region. We seamlessly integrated domain adaptation techniques by meticulously fine-tuning pre-existing Masked Language Models on a diverse array of informal texts. We employed a multifaceted approach, leveraging Transfer Learning, Stacking, and Ensemble techniques to enhance our model’s performance. Our integrated system, amalgamating the refined BanglaBERT model through MLM and our Weighted Ensemble approach, showcased superior efficacy, achieving macro F1 scores of 71% and 72%, respectively, while the MLM approach secured the 18th position among participants. This underscores the robustness and precision of our proposed paradigm in the nuanced detection and categorization of violent narratives within digital realms.
%R 10.18653/v1/2023.banglalp-1.25
%U https://aclanthology.org/2023.banglalp-1.25
%U https://doi.org/10.18653/v1/2023.banglalp-1.25
%P 208-213
Markdown (Informal)
[SUST_Black Box at BLP-2023 Task 1: Detecting Communal Violence in Texts: An Exploration of MLM and Weighted Ensemble Techniques](https://aclanthology.org/2023.banglalp-1.25) (Shibu et al., BanglaLP 2023)
ACL