@inproceedings{chatterjee-etal-2023-vaclm,
title = "{V}ac{LM} at {BLP}-2023 Task 1: Leveraging {BERT} models for Violence detection in {B}angla",
author = "Chatterjee, Shilpa and
Evenss, P J Leo and
Bhattacharyya, Pramit",
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-1.23",
doi = "10.18653/v1/2023.banglalp-1.23",
pages = "196--200",
abstract = "This study introduces the system submitted to the BLP Shared Task 1: Violence Inciting Text Detection (VITD) by the VacLM team. In this work, we analyzed the impact of various transformer-based models for detecting violence in texts. BanglaBERT outperforms all the other competing models. We also observed that the transformer-based models are not adept at classifying Passive Violence and Direct Violence class but can better detect violence in texts, which was the task{'}s primary objective. On the shared task, we secured a rank of 12 with macro F1-score of 72.656{\%}.",
}
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<abstract>This study introduces the system submitted to the BLP Shared Task 1: Violence Inciting Text Detection (VITD) by the VacLM team. In this work, we analyzed the impact of various transformer-based models for detecting violence in texts. BanglaBERT outperforms all the other competing models. We also observed that the transformer-based models are not adept at classifying Passive Violence and Direct Violence class but can better detect violence in texts, which was the task’s primary objective. On the shared task, we secured a rank of 12 with macro F1-score of 72.656%.</abstract>
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%0 Conference Proceedings
%T VacLM at BLP-2023 Task 1: Leveraging BERT models for Violence detection in Bangla
%A Chatterjee, Shilpa
%A Evenss, P. J. Leo
%A Bhattacharyya, Pramit
%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 chatterjee-etal-2023-vaclm
%X This study introduces the system submitted to the BLP Shared Task 1: Violence Inciting Text Detection (VITD) by the VacLM team. In this work, we analyzed the impact of various transformer-based models for detecting violence in texts. BanglaBERT outperforms all the other competing models. We also observed that the transformer-based models are not adept at classifying Passive Violence and Direct Violence class but can better detect violence in texts, which was the task’s primary objective. On the shared task, we secured a rank of 12 with macro F1-score of 72.656%.
%R 10.18653/v1/2023.banglalp-1.23
%U https://aclanthology.org/2023.banglalp-1.23
%U https://doi.org/10.18653/v1/2023.banglalp-1.23
%P 196-200
Markdown (Informal)
[VacLM at BLP-2023 Task 1: Leveraging BERT models for Violence detection in Bangla](https://aclanthology.org/2023.banglalp-1.23) (Chatterjee et al., BanglaLP 2023)
ACL