SUST_Black Box at BLP-2023 Task 1: Detecting Communal Violence in Texts: An Exploration of MLM and Weighted Ensemble Techniques

Hrithik Shibu, Shrestha Datta, Zhalok Rahman, Shahrab Sami, Md. Sumon Miah, Raisa Fairooz, Md Mollah

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.
Anthology ID:
2023.banglalp-1.25
Volume:
Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)
Month:
December
Year:
2023
Address:
Singapore
Editors:
Firoj Alam, Sudipta Kar, Shammur Absar Chowdhury, Farig Sadeque, Ruhul Amin
Venue:
BanglaLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
208–213
Language:
URL:
https://aclanthology.org/2023.banglalp-1.25
DOI:
10.18653/v1/2023.banglalp-1.25
Bibkey:
Cite (ACL):
Hrithik Shibu, Shrestha Datta, Zhalok Rahman, Shahrab Sami, Md. Sumon Miah, Raisa Fairooz, and Md Mollah. 2023. SUST_Black Box at BLP-2023 Task 1: Detecting Communal Violence in Texts: An Exploration of MLM and Weighted Ensemble Techniques. In Proceedings of the First Workshop on Bangla Language Processing (BLP-2023), pages 208–213, Singapore. Association for Computational Linguistics.
Cite (Informal):
SUST_Black Box at BLP-2023 Task 1: Detecting Communal Violence in Texts: An Exploration of MLM and Weighted Ensemble Techniques (Shibu et al., BanglaLP 2023)
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