@inproceedings{laureano-de-leon-etal-2025-uob,
    title = "{U}o{B}-{NLP} at {S}em{E}val-2025 Task 11: Leveraging Adapters for Multilingual and Cross-Lingual Emotion Detection",
    author = "Laureano De Leon, Frances Adriana  and
      Wang, Yixiao  and
      Feng, Yue  and
      Lee, Mark",
    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-1.207/",
    pages = "1570--1576",
    ISBN = "979-8-89176-273-2",
    abstract = "Emotion detection in natural language processing is a challenging task due to the complexity of human emotions and linguistic diversity. While significant progress has been made in high-resource languages, emotion detection in low-resource languages remains underexplored. In this work, we address multilingual and cross-lingual emotion detection by leveraging adapter-based fine-tuning with multilingual pre-trained language models. Adapters introduce a small number of trainable parameters while keeping the pre-trained model weights fixed, offering a parameter-efficient approach to adaptation. We experiment with different adapter tuning strategies, including task-only adapters, target-language-ready task adapters, and language-family-based adapters. Our results show that target-language-ready task adapters achieve the best overall performance, particularly for low-resource African languages with our team ranking 7th for Tigrinya, and 8th for Kinyarwanda. In Track C, our system ranked 5th for Oromo, Tigrinya, Kinyarwanda, Amharic, and Igbo. Our approach outperforms large language models in 11 languages and matches their performance in four others, despite using significantly fewer parameters. Furthermore, we find that adapter-based models retain cross-linguistic transfer capabilities while requiring fewer computational resources compared to full fine-tuning for each language."
}