@inproceedings{kesanam-etal-2025-nitk,
title = "{NITK}-{VITAL} at {S}em{E}val-2025 Task 11: Focal-{R}o{BERT}a: Addressing Class Imbalance in Multi-Label Emotion Classification",
author = "Kesanam, Ashinee and
Venkata Ravi Ram, Gummuluri and
Banoth, Chaithanya Swaroop and
Reddy, G Rama Mohana",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.142/",
pages = "1077--1081",
ISBN = "979-8-89176-273-2",
abstract = "This paper presents our approach to SemEval Task 11, which focuses on multi-label emotion detection in English textual data. We experimented with multiple methodologies, including traditional machine learning models, deep learning architectures, and transformer-based models. Our best-performing approach employed RoBERTa with focal loss, which effectively mitigated class imbalances and achieved a macro F1-score of 0.7563, outperforming other techniques. Comparative analyses between different embedding strategies, such as TF-IDF, BERT, and MiniLM, revealed that transformer-based models consistently provided superior performance. The results demonstrate the effectiveness of focal loss in handling highly skewed emotion distributions. Our system contributes to advancing multi-label emotion detection by leveraging robust pre-trained models and loss function optimization."
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<abstract>This paper presents our approach to SemEval Task 11, which focuses on multi-label emotion detection in English textual data. We experimented with multiple methodologies, including traditional machine learning models, deep learning architectures, and transformer-based models. Our best-performing approach employed RoBERTa with focal loss, which effectively mitigated class imbalances and achieved a macro F1-score of 0.7563, outperforming other techniques. Comparative analyses between different embedding strategies, such as TF-IDF, BERT, and MiniLM, revealed that transformer-based models consistently provided superior performance. The results demonstrate the effectiveness of focal loss in handling highly skewed emotion distributions. Our system contributes to advancing multi-label emotion detection by leveraging robust pre-trained models and loss function optimization.</abstract>
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%0 Conference Proceedings
%T NITK-VITAL at SemEval-2025 Task 11: Focal-RoBERTa: Addressing Class Imbalance in Multi-Label Emotion Classification
%A Kesanam, Ashinee
%A Venkata Ravi Ram, Gummuluri
%A Banoth, Chaithanya Swaroop
%A Reddy, G. Rama Mohana
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F kesanam-etal-2025-nitk
%X This paper presents our approach to SemEval Task 11, which focuses on multi-label emotion detection in English textual data. We experimented with multiple methodologies, including traditional machine learning models, deep learning architectures, and transformer-based models. Our best-performing approach employed RoBERTa with focal loss, which effectively mitigated class imbalances and achieved a macro F1-score of 0.7563, outperforming other techniques. Comparative analyses between different embedding strategies, such as TF-IDF, BERT, and MiniLM, revealed that transformer-based models consistently provided superior performance. The results demonstrate the effectiveness of focal loss in handling highly skewed emotion distributions. Our system contributes to advancing multi-label emotion detection by leveraging robust pre-trained models and loss function optimization.
%U https://aclanthology.org/2025.semeval-1.142/
%P 1077-1081
Markdown (Informal)
[NITK-VITAL at SemEval-2025 Task 11: Focal-RoBERTa: Addressing Class Imbalance in Multi-Label Emotion Classification](https://aclanthology.org/2025.semeval-1.142/) (Kesanam et al., SemEval 2025)
ACL