@inproceedings{saeedi-etal-2025-gt,
title = "{GT}-{NLP} at {S}em{E}val-2025 Task 11: {E}mo{R}ationale, Evidence-Based Emotion Detection via Retrieval-Augmented Generation",
author = "Saeedi, Daniel and
Kheirandish, Alireza and
Saeedi, Sirwe and
Sahour, Hossein and
Panahi, Aliakbar and
Naeeni, Iman",
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-1.90/",
pages = "640--650",
ISBN = "979-8-89176-273-2",
abstract = "Emotion detection in multilingual settings presents significant challenges, particularly for low-resource languages where labeled datasets are scarce. To address these limitations, we introduce EmoRationale, a Retrieval-Augmented Generation (RAG) framework designed to enhance explainability and cross-lingual generalization in emotion detection. Our approach combines vector-based retrieval with in-context learning in large language models (LLMs), using semantically relevant examples to enhance classification accuracy and interpretability. Unlike traditional fine-tuning methods, our system provides evidence-based reasoning for its predictions, making emotion detection more transparent and adaptable across diverse linguistic contexts. Experimental results on the SemEval-2025 Task 11 dataset demonstrate that our RAG-based method achieves strong performance in multi-label emotion classification, emotion intensity assessment, and cross-lingual emotion transfer, surpassing conventional models in interpretability while remaining cost-effective."
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<abstract>Emotion detection in multilingual settings presents significant challenges, particularly for low-resource languages where labeled datasets are scarce. To address these limitations, we introduce EmoRationale, a Retrieval-Augmented Generation (RAG) framework designed to enhance explainability and cross-lingual generalization in emotion detection. Our approach combines vector-based retrieval with in-context learning in large language models (LLMs), using semantically relevant examples to enhance classification accuracy and interpretability. Unlike traditional fine-tuning methods, our system provides evidence-based reasoning for its predictions, making emotion detection more transparent and adaptable across diverse linguistic contexts. Experimental results on the SemEval-2025 Task 11 dataset demonstrate that our RAG-based method achieves strong performance in multi-label emotion classification, emotion intensity assessment, and cross-lingual emotion transfer, surpassing conventional models in interpretability while remaining cost-effective.</abstract>
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%0 Conference Proceedings
%T GT-NLP at SemEval-2025 Task 11: EmoRationale, Evidence-Based Emotion Detection via Retrieval-Augmented Generation
%A Saeedi, Daniel
%A Kheirandish, Alireza
%A Saeedi, Sirwe
%A Sahour, Hossein
%A Panahi, Aliakbar
%A Naeeni, Iman
%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 saeedi-etal-2025-gt
%X Emotion detection in multilingual settings presents significant challenges, particularly for low-resource languages where labeled datasets are scarce. To address these limitations, we introduce EmoRationale, a Retrieval-Augmented Generation (RAG) framework designed to enhance explainability and cross-lingual generalization in emotion detection. Our approach combines vector-based retrieval with in-context learning in large language models (LLMs), using semantically relevant examples to enhance classification accuracy and interpretability. Unlike traditional fine-tuning methods, our system provides evidence-based reasoning for its predictions, making emotion detection more transparent and adaptable across diverse linguistic contexts. Experimental results on the SemEval-2025 Task 11 dataset demonstrate that our RAG-based method achieves strong performance in multi-label emotion classification, emotion intensity assessment, and cross-lingual emotion transfer, surpassing conventional models in interpretability while remaining cost-effective.
%U https://aclanthology.org/2025.semeval-1.90/
%P 640-650
Markdown (Informal)
[GT-NLP at SemEval-2025 Task 11: EmoRationale, Evidence-Based Emotion Detection via Retrieval-Augmented Generation](https://aclanthology.org/2025.semeval-1.90/) (Saeedi et al., SemEval 2025)
ACL