@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://preview.aclanthology.org/transition-to-people-yaml/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."
}