@inproceedings{veeramani-etal-2023-lowresourcenlu,
title = "{L}ow{R}esource{NLU} at {BLP}-2023 Task 1 {\&} 2: Enhancing Sentiment Classification and Violence Incitement Detection in {B}angla Through Aggregated Language Models",
author = "Veeramani, Hariram and
Thapa, Surendrabikram and
Naseem, Usman",
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-1.29",
doi = "10.18653/v1/2023.banglalp-1.29",
pages = "230--235",
abstract = "Violence incitement detection and sentiment analysis hold significant importance in the field of natural language processing. However, in the case of the Bangla language, there are unique challenges due to its low-resource nature. In this paper, we address these challenges by presenting an innovative approach that leverages aggregated BERT models for two tasks at the BLP workshop in EMNLP 2023, specifically tailored for Bangla. Task 1 focuses on violence-inciting text detection, while task 2 centers on sentiment analysis. Our approach combines fine-tuning with textual entailment (utilizing BanglaBERT), Masked Language Model (MLM) training (making use of BanglaBERT), and the use of standalone Multilingual BERT. This comprehensive framework significantly enhances the accuracy of sentiment classification and violence incitement detection in Bangla text. Our method achieved the 11th rank in task 1 with an F1-score of 73.47 and the 4th rank in task 2 with an F1-score of 71.73. This paper provides a detailed system description along with an analysis of the impact of each component of our framework.",
}
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<abstract>Violence incitement detection and sentiment analysis hold significant importance in the field of natural language processing. However, in the case of the Bangla language, there are unique challenges due to its low-resource nature. In this paper, we address these challenges by presenting an innovative approach that leverages aggregated BERT models for two tasks at the BLP workshop in EMNLP 2023, specifically tailored for Bangla. Task 1 focuses on violence-inciting text detection, while task 2 centers on sentiment analysis. Our approach combines fine-tuning with textual entailment (utilizing BanglaBERT), Masked Language Model (MLM) training (making use of BanglaBERT), and the use of standalone Multilingual BERT. This comprehensive framework significantly enhances the accuracy of sentiment classification and violence incitement detection in Bangla text. Our method achieved the 11th rank in task 1 with an F1-score of 73.47 and the 4th rank in task 2 with an F1-score of 71.73. This paper provides a detailed system description along with an analysis of the impact of each component of our framework.</abstract>
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%0 Conference Proceedings
%T LowResourceNLU at BLP-2023 Task 1 & 2: Enhancing Sentiment Classification and Violence Incitement Detection in Bangla Through Aggregated Language Models
%A Veeramani, Hariram
%A Thapa, Surendrabikram
%A Naseem, Usman
%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 veeramani-etal-2023-lowresourcenlu
%X Violence incitement detection and sentiment analysis hold significant importance in the field of natural language processing. However, in the case of the Bangla language, there are unique challenges due to its low-resource nature. In this paper, we address these challenges by presenting an innovative approach that leverages aggregated BERT models for two tasks at the BLP workshop in EMNLP 2023, specifically tailored for Bangla. Task 1 focuses on violence-inciting text detection, while task 2 centers on sentiment analysis. Our approach combines fine-tuning with textual entailment (utilizing BanglaBERT), Masked Language Model (MLM) training (making use of BanglaBERT), and the use of standalone Multilingual BERT. This comprehensive framework significantly enhances the accuracy of sentiment classification and violence incitement detection in Bangla text. Our method achieved the 11th rank in task 1 with an F1-score of 73.47 and the 4th rank in task 2 with an F1-score of 71.73. This paper provides a detailed system description along with an analysis of the impact of each component of our framework.
%R 10.18653/v1/2023.banglalp-1.29
%U https://aclanthology.org/2023.banglalp-1.29
%U https://doi.org/10.18653/v1/2023.banglalp-1.29
%P 230-235
Markdown (Informal)
[LowResourceNLU at BLP-2023 Task 1 & 2: Enhancing Sentiment Classification and Violence Incitement Detection in Bangla Through Aggregated Language Models](https://aclanthology.org/2023.banglalp-1.29) (Veeramani et al., BanglaLP 2023)
ACL