@inproceedings{raihan-etal-2024-cuet,
title = "{CUET}{\_}{DUO}@{S}tress{I}dent{\_}{LT}-{EDI}@{EACL}2024: Stress Identification Using {T}amil-{T}elugu {BERT}",
author = "Raihan, Abu and
Rahman, Tanzim and
Rahman, Md. and
Hossain, Jawad and
Ahsan, Shawly and
Das, Avishek and
Hoque, Mohammed Moshiul",
editor = {Chakravarthi, Bharathi Raja and
B, Bharathi and
Buitelaar, Paul and
Durairaj, Thenmozhi and
Kov{\'a}cs, Gy{\"o}rgy and
Garc{\'\i}a Cumbreras, Miguel {\'A}ngel},
booktitle = "Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion",
month = mar,
year = "2024",
address = "St. Julian's, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.ltedi-1.35",
pages = "265--270",
abstract = "The pervasive impact of stress on individuals necessitates proactive identification and intervention measures, especially in social media interaction. This research paper addresses the imperative need for proactive identification and intervention concerning the widespread influence of stress on individuals. This study focuses on the shared task, {``}Stress Identification in Dravidian Languages,{''} specifically emphasizing Tamil and Telugu code-mixed languages. The primary objective of the task is to classify social media messages into two categories: stressed and non stressed. We employed various methodologies, from traditional machine-learning techniques to state-of-the-art transformer-based models. Notably, the Tamil-BERT and Telugu-BERT models exhibited exceptional performance, achieving a noteworthy macro F1-score of \textbf{0.71} and \textbf{0.72}, respectively, and securing the $15^{th}$ position in Tamil code-mixed language and the $9^{th}$ position in the Telugu code-mixed language. These findings underscore the effectiveness of these models in recognizing stress signals within social media content composed in Tamil and Telugu.",
}
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<abstract>The pervasive impact of stress on individuals necessitates proactive identification and intervention measures, especially in social media interaction. This research paper addresses the imperative need for proactive identification and intervention concerning the widespread influence of stress on individuals. This study focuses on the shared task, “Stress Identification in Dravidian Languages,” specifically emphasizing Tamil and Telugu code-mixed languages. The primary objective of the task is to classify social media messages into two categories: stressed and non stressed. We employed various methodologies, from traditional machine-learning techniques to state-of-the-art transformer-based models. Notably, the Tamil-BERT and Telugu-BERT models exhibited exceptional performance, achieving a noteworthy macro F1-score of 0.71 and 0.72, respectively, and securing the 15^th position in Tamil code-mixed language and the 9^th position in the Telugu code-mixed language. These findings underscore the effectiveness of these models in recognizing stress signals within social media content composed in Tamil and Telugu.</abstract>
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%0 Conference Proceedings
%T CUET_DUO@StressIdent_LT-EDI@EACL2024: Stress Identification Using Tamil-Telugu BERT
%A Raihan, Abu
%A Rahman, Tanzim
%A Rahman, Md.
%A Hossain, Jawad
%A Ahsan, Shawly
%A Das, Avishek
%A Hoque, Mohammed Moshiul
%Y Chakravarthi, Bharathi Raja
%Y B, Bharathi
%Y Buitelaar, Paul
%Y Durairaj, Thenmozhi
%Y Kovács, György
%Y García Cumbreras, Miguel Ángel
%S Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F raihan-etal-2024-cuet
%X The pervasive impact of stress on individuals necessitates proactive identification and intervention measures, especially in social media interaction. This research paper addresses the imperative need for proactive identification and intervention concerning the widespread influence of stress on individuals. This study focuses on the shared task, “Stress Identification in Dravidian Languages,” specifically emphasizing Tamil and Telugu code-mixed languages. The primary objective of the task is to classify social media messages into two categories: stressed and non stressed. We employed various methodologies, from traditional machine-learning techniques to state-of-the-art transformer-based models. Notably, the Tamil-BERT and Telugu-BERT models exhibited exceptional performance, achieving a noteworthy macro F1-score of 0.71 and 0.72, respectively, and securing the 15^th position in Tamil code-mixed language and the 9^th position in the Telugu code-mixed language. These findings underscore the effectiveness of these models in recognizing stress signals within social media content composed in Tamil and Telugu.
%U https://aclanthology.org/2024.ltedi-1.35
%P 265-270
Markdown (Informal)
[CUET_DUO@StressIdent_LT-EDI@EACL2024: Stress Identification Using Tamil-Telugu BERT](https://aclanthology.org/2024.ltedi-1.35) (Raihan et al., LTEDI-WS 2024)
ACL
- Abu Raihan, Tanzim Rahman, Md. Rahman, Jawad Hossain, Shawly Ahsan, Avishek Das, and Mohammed Moshiul Hoque. 2024. CUET_DUO@StressIdent_LT-EDI@EACL2024: Stress Identification Using Tamil-Telugu BERT. In Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion, pages 265–270, St. Julian's, Malta. Association for Computational Linguistics.