@inproceedings{mukherjee-etal-2023-ufal,
title = "{UFAL}-{ULD} at {BLP}-2023 Task 1: Violence Detection in {B}angla Text",
author = "Mukherjee, Sourabrata and
Ojha, Atul Kr. and
Du{\v{s}}ek, Ond{\v{r}}ej",
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-1.27",
doi = "10.18653/v1/2023.banglalp-1.27",
pages = "220--224",
abstract = "In this paper, we present UFAL-ULD team{'}s system, desinged as a part of the BLP Shared Task 1: Violence Inciting Text Detection (VITD). This task aims to classify text, with a particular challenge of identifying incitement to violence into Direct, Indirect or Non-violence levels. We experimented with several pre-trained sequence classification models, including XLM-RoBERTa, BanglaBERT, Bangla BERT Base, and Multilingual BERT. Our best-performing model was based on the XLM-RoBERTa-base architecture, which outperformed the baseline models. Our system was ranked 20th among the 27 teams that participated in the task.",
}
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<abstract>In this paper, we present UFAL-ULD team’s system, desinged as a part of the BLP Shared Task 1: Violence Inciting Text Detection (VITD). This task aims to classify text, with a particular challenge of identifying incitement to violence into Direct, Indirect or Non-violence levels. We experimented with several pre-trained sequence classification models, including XLM-RoBERTa, BanglaBERT, Bangla BERT Base, and Multilingual BERT. Our best-performing model was based on the XLM-RoBERTa-base architecture, which outperformed the baseline models. Our system was ranked 20th among the 27 teams that participated in the task.</abstract>
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%0 Conference Proceedings
%T UFAL-ULD at BLP-2023 Task 1: Violence Detection in Bangla Text
%A Mukherjee, Sourabrata
%A Ojha, Atul Kr.
%A Dušek, Ondřej
%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 mukherjee-etal-2023-ufal
%X In this paper, we present UFAL-ULD team’s system, desinged as a part of the BLP Shared Task 1: Violence Inciting Text Detection (VITD). This task aims to classify text, with a particular challenge of identifying incitement to violence into Direct, Indirect or Non-violence levels. We experimented with several pre-trained sequence classification models, including XLM-RoBERTa, BanglaBERT, Bangla BERT Base, and Multilingual BERT. Our best-performing model was based on the XLM-RoBERTa-base architecture, which outperformed the baseline models. Our system was ranked 20th among the 27 teams that participated in the task.
%R 10.18653/v1/2023.banglalp-1.27
%U https://aclanthology.org/2023.banglalp-1.27
%U https://doi.org/10.18653/v1/2023.banglalp-1.27
%P 220-224
Markdown (Informal)
[UFAL-ULD at BLP-2023 Task 1: Violence Detection in Bangla Text](https://aclanthology.org/2023.banglalp-1.27) (Mukherjee et al., BanglaLP 2023)
ACL