@inproceedings{bourbour-etal-2025-tarbiat,
title = "{T}arbiat{\_}{M}odares{\_}{S}em{E}val2025{\_}{T}ask11{\_}{M}ulti{L}abel{\_}{E}motion{\_}{T}ransfer{L}earning",
author = "Bourbour, Sara and
Gheysari, Maryam and
Saeidi Kelishami, Amin and
Talaei, Tahereh and
Rahimzadeh, Fatemeh and
Moeini, Erfan",
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-1.154/",
pages = "1168--1173",
ISBN = "979-8-89176-273-2",
abstract = "The SemEval-2025 Task 11 addresses multi-label emotion detection, classifying perceived emotions in text. Our system targets Amharic, a morphologically complex, low-resource language. We fine-tune LaBSE with class-weighted loss for multi-label prediction.Our architecture consists of: (i) text tokenization via LaBSE, (ii) a fully connected layer with sigmoid activation for classification, and (iii) optimization using BCEWithLogitsLoss and AdamW. Ablation studies on class balancing and data augmentation showed that simple upsampling did not improve performance, highlighting the need for more sophisticated techniques.Our system ranked 14th out of 43 teams, achieving 0.4938 accuracy, 0.6931 micro-F1, and 0.6450 macro-F1, surpassing the task baseline (0.6383 macro-F1). Error analysis revealed that anger and disgust were well detected, while fear and surprise were frequently misclassified due to overlapping linguistic cues. Our findings underscore the challenges of multi-label emotion detection in low-resource languages. Future work could explore context-aware embeddings, improved data augmentation, and adaptive loss functions."
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<abstract>The SemEval-2025 Task 11 addresses multi-label emotion detection, classifying perceived emotions in text. Our system targets Amharic, a morphologically complex, low-resource language. We fine-tune LaBSE with class-weighted loss for multi-label prediction.Our architecture consists of: (i) text tokenization via LaBSE, (ii) a fully connected layer with sigmoid activation for classification, and (iii) optimization using BCEWithLogitsLoss and AdamW. Ablation studies on class balancing and data augmentation showed that simple upsampling did not improve performance, highlighting the need for more sophisticated techniques.Our system ranked 14th out of 43 teams, achieving 0.4938 accuracy, 0.6931 micro-F1, and 0.6450 macro-F1, surpassing the task baseline (0.6383 macro-F1). Error analysis revealed that anger and disgust were well detected, while fear and surprise were frequently misclassified due to overlapping linguistic cues. Our findings underscore the challenges of multi-label emotion detection in low-resource languages. Future work could explore context-aware embeddings, improved data augmentation, and adaptive loss functions.</abstract>
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%0 Conference Proceedings
%T Tarbiat_Modares_SemEval2025_Task11_MultiLabel_Emotion_TransferLearning
%A Bourbour, Sara
%A Gheysari, Maryam
%A Saeidi Kelishami, Amin
%A Talaei, Tahereh
%A Rahimzadeh, Fatemeh
%A Moeini, Erfan
%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 bourbour-etal-2025-tarbiat
%X The SemEval-2025 Task 11 addresses multi-label emotion detection, classifying perceived emotions in text. Our system targets Amharic, a morphologically complex, low-resource language. We fine-tune LaBSE with class-weighted loss for multi-label prediction.Our architecture consists of: (i) text tokenization via LaBSE, (ii) a fully connected layer with sigmoid activation for classification, and (iii) optimization using BCEWithLogitsLoss and AdamW. Ablation studies on class balancing and data augmentation showed that simple upsampling did not improve performance, highlighting the need for more sophisticated techniques.Our system ranked 14th out of 43 teams, achieving 0.4938 accuracy, 0.6931 micro-F1, and 0.6450 macro-F1, surpassing the task baseline (0.6383 macro-F1). Error analysis revealed that anger and disgust were well detected, while fear and surprise were frequently misclassified due to overlapping linguistic cues. Our findings underscore the challenges of multi-label emotion detection in low-resource languages. Future work could explore context-aware embeddings, improved data augmentation, and adaptive loss functions.
%U https://aclanthology.org/2025.semeval-1.154/
%P 1168-1173
Markdown (Informal)
[Tarbiat_Modares_SemEval2025_Task11_MultiLabel_Emotion_TransferLearning](https://aclanthology.org/2025.semeval-1.154/) (Bourbour et al., SemEval 2025)
ACL
- Sara Bourbour, Maryam Gheysari, Amin Saeidi Kelishami, Tahereh Talaei, Fatemeh Rahimzadeh, and Erfan Moeini. 2025. Tarbiat_Modares_SemEval2025_Task11_MultiLabel_Emotion_TransferLearning. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 1168–1173, Vienna, Austria. Association for Computational Linguistics.