@inproceedings{pham-hoang-le-etal-2025-nta,
    title = "{NTA} at {S}em{E}val-2025 Task 11: Enhanced Multilingual Textual Multi-label Emotion Detection via Integrated Augmentation Learning",
    author = "Pham Hoang Le, Nguyen  and
      Nguyen Tran Khuong, An  and
      Nguyen Thi Ngoc, Tram  and
      Dang Van, Thin",
    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-1.237/",
    pages = "1795--1801",
    ISBN = "979-8-89176-273-2",
    abstract = "Emotion detection in text is crucial for various applications, but progress, especially in multi-label scenarios, is often hampered by data scarcity, particularly for low-resource languages like Emakhuwa and Tigrinya. This lack of data limits model performance and generalizability. To address this, the NTA team developed a system for SemEval-2025 Task 11, leveraging data augmentation techniques: swap, deletion, oversampling, emotion-focused synonym insertion and synonym replacement to enhance baseline models for multilingual textual multi-label emotion detection. Our proposed system achieved significantly higher macro F1-scores compared to the baseline across multiple languages."
}