@inproceedings{paran-etal-2025-zero,
    title = "{Z}ero{\_}{S}hot at {S}em{E}val-2025 Task 11: Fine-Tuning Deep Learning and Transformer-based Models for Emotion Detection in Multi-label Classification, Intensity Estimation, and Cross-lingual Adaptation",
    author = "Paran, Ashraful Islam  and
      Aftahee, Sabik  and
      Hossan, Md. Refaj  and
      Hossain, Jawad  and
      Hoque, Mohammed Moshiul",
    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/corrections-2025-08/2025.semeval-1.247/",
    pages = "1890--1904",
    ISBN = "979-8-89176-273-2",
    abstract = "Language is a rich medium employed to convey emotions subtly and intricately, as abundant as human emotional experiences themselves. Emotion recognition in natural language processing (NLP) is now a core element in facilitating human-computer interaction and interpreting intricate human behavior via text. It has potential applications in every sector i.e., sentiment analysis, mental health surveillance. However, prior research on emotion recognition is primarily from high-resource languages while low-resource languages (LRLs) are not well represented. This disparity has been a limitation to the development of universally applicable emotion detection models. To address this, the SemEval-2025 Shared Task 11 focused on perceived emotions, aiming to identify the emotions conveyed by a text snippet. It includes three tracks: Multi-label Emotion Detection (Track A), Emotion Intensity (Track B), and Cross-lingual Emotion Detection (Track C). This paper explores various models, including machine learning (LR, SVM, RF, NB), deep learning (BiLSTM+CNN, BiLSTM+BiGRU), and transformer-based models (XLM-R, mBERT, ModernBERT). The results showed that XLM-R outperformed other models in Tracks A and B, while BiLSTM+CNN performed better for Track C across most languages."
}