@inproceedings{ince-aryal-2025-howard,
title = "{H}oward {U}niversity-{AI}4{PC} at {S}em{E}val-2025 Task 11: Combining Expert Personas via Prompting for Enhanced Multilingual Emotion Analysis",
author = "Ince, Amir and
Aryal, Saurav",
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-1.216/",
pages = "1645--1655",
ISBN = "979-8-89176-273-2",
abstract = "For our approach to SemEval-2025 Task 11, we employ a multi-tier evaluation framework for perceived emotion analysis. Our system consists of a smaller-parameter-size large language model that independently predicts a given text{'}s perceived emotion while explaining the reasoning behind its decision. The initial model{'}s persona is varied through careful prompting, allowing it to represent multiple perspectives. These outputs, including both predictions and reasoning, are aggregated and fed into a final decision-making model that determines the ultimate emotion classification. We evaluated our approach in official SemEval Task 11 on subtasks A and C in all the languages provided."
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<abstract>For our approach to SemEval-2025 Task 11, we employ a multi-tier evaluation framework for perceived emotion analysis. Our system consists of a smaller-parameter-size large language model that independently predicts a given text’s perceived emotion while explaining the reasoning behind its decision. The initial model’s persona is varied through careful prompting, allowing it to represent multiple perspectives. These outputs, including both predictions and reasoning, are aggregated and fed into a final decision-making model that determines the ultimate emotion classification. We evaluated our approach in official SemEval Task 11 on subtasks A and C in all the languages provided.</abstract>
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%0 Conference Proceedings
%T Howard University-AI4PC at SemEval-2025 Task 11: Combining Expert Personas via Prompting for Enhanced Multilingual Emotion Analysis
%A Ince, Amir
%A Aryal, Saurav
%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 ince-aryal-2025-howard
%X For our approach to SemEval-2025 Task 11, we employ a multi-tier evaluation framework for perceived emotion analysis. Our system consists of a smaller-parameter-size large language model that independently predicts a given text’s perceived emotion while explaining the reasoning behind its decision. The initial model’s persona is varied through careful prompting, allowing it to represent multiple perspectives. These outputs, including both predictions and reasoning, are aggregated and fed into a final decision-making model that determines the ultimate emotion classification. We evaluated our approach in official SemEval Task 11 on subtasks A and C in all the languages provided.
%U https://aclanthology.org/2025.semeval-1.216/
%P 1645-1655
Markdown (Informal)
[Howard University-AI4PC at SemEval-2025 Task 11: Combining Expert Personas via Prompting for Enhanced Multilingual Emotion Analysis](https://aclanthology.org/2025.semeval-1.216/) (Ince & Aryal, SemEval 2025)
ACL