@inproceedings{saha-nanda-2023-banglanlp,
title = "{B}angla{NLP} at {BLP}-2023 Task 1: Benchmarking different Transformer Models for Violence Inciting Text Detection in {B}angla",
author = "Saha, Saumajit and
Nanda, Albert",
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-1.17",
doi = "10.18653/v1/2023.banglalp-1.17",
pages = "163--167",
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.",
}
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%0 Conference Proceedings
%T BanglaNLP at BLP-2023 Task 1: Benchmarking different Transformer Models for Violence Inciting Text Detection in Bangla
%A Saha, Saumajit
%A Nanda, Albert
%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 saha-nanda-2023-banglanlp
%X 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.
%R 10.18653/v1/2023.banglalp-1.17
%U https://aclanthology.org/2023.banglalp-1.17
%U https://doi.org/10.18653/v1/2023.banglalp-1.17
%P 163-167
Markdown (Informal)
[BanglaNLP at BLP-2023 Task 1: Benchmarking different Transformer Models for Violence Inciting Text Detection in Bangla](https://aclanthology.org/2023.banglalp-1.17) (Saha & Nanda, BanglaLP 2023)
ACL