@inproceedings{li-etal-2025-ynu,
title = "{YNU}-{HPCC} at {S}em{E}val-2025 Task 2: Local Cache and Online Retrieval-Based method for Entity-Aware Machine Translation",
author = "Li, Hao and
Wang, Jin and
Zhang, Xuejie",
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-1.66/",
pages = "476--481",
ISBN = "979-8-89176-273-2",
abstract = "This paper presents methods for {\{}textbf{\{}SemEval-2025 Task 11{\}}{\}} on text-based emotion detection across three tracks: Multi-label Emotion Detection, Emotion Intensity Prediction, and Cross-lingual Emotion Detection. We apply approaches such as supervised fine-tuning, preference-based reinforcement learning, and few-shot learning to enhance performance. Our combined strategies result in improved accuracy, particularly in multi-label and cross-lingual emotion detection, demonstrating the effectiveness of these methods in diverse linguistic settings."
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<abstract>This paper presents methods for {textbf{SemEval-2025 Task 11}} on text-based emotion detection across three tracks: Multi-label Emotion Detection, Emotion Intensity Prediction, and Cross-lingual Emotion Detection. We apply approaches such as supervised fine-tuning, preference-based reinforcement learning, and few-shot learning to enhance performance. Our combined strategies result in improved accuracy, particularly in multi-label and cross-lingual emotion detection, demonstrating the effectiveness of these methods in diverse linguistic settings.</abstract>
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%0 Conference Proceedings
%T YNU-HPCC at SemEval-2025 Task 2: Local Cache and Online Retrieval-Based method for Entity-Aware Machine Translation
%A Li, Hao
%A Wang, Jin
%A Zhang, Xuejie
%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 li-etal-2025-ynu
%X This paper presents methods for {textbf{SemEval-2025 Task 11}} on text-based emotion detection across three tracks: Multi-label Emotion Detection, Emotion Intensity Prediction, and Cross-lingual Emotion Detection. We apply approaches such as supervised fine-tuning, preference-based reinforcement learning, and few-shot learning to enhance performance. Our combined strategies result in improved accuracy, particularly in multi-label and cross-lingual emotion detection, demonstrating the effectiveness of these methods in diverse linguistic settings.
%U https://aclanthology.org/2025.semeval-1.66/
%P 476-481Markdown (Informal)
[YNU-HPCC at SemEval-2025 Task 2: Local Cache and Online Retrieval-Based method for Entity-Aware Machine Translation](https://aclanthology.org/2025.semeval-1.66/) (Li et al., SemEval 2025)
ACL