@inproceedings{ahmad-etal-2025-csecu-learners,
    title = "{CSECU}-Learners at {S}em{E}val-2025 Task 11: Multilingual Emotion Recognition and Intensity Prediction with Language-tuned Transformers and Multi-sample Dropout",
    author = "Ahmad, Monir  and
      Azim, Muhammad Anwarul  and
      Chy, Abu Nowshed",
    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-1.178/",
    pages = "1332--1341",
    ISBN = "979-8-89176-273-2",
    abstract = "In today{'}s digital era, individuals convey their feelings, viewpoints, and perspectives across various platforms in nuanced and intricate ways. At times, these expressions can be challenging to articulate and interpret. Emotion recognition aims to identify the most relevant emotions in a text that accurately represent the author{'}s psychological state. Despite its substantial impact on natural language processing (NLP), this task has primarily been researched only in high-resource languages. To bridge this gap, SemEval-2025 Task 11 introduces a multilingual emotion recognition challenge encompassing 32 languages, promoting broader linguistic inclusivity in emotion recognition. This paper presents our participation in this task, where we introduce a language-specific fine-tuned transformer-based system for emotion recognition and emotion intensity prediction. To enhance generalization, we incorporate a multi-sample dropout strategy. Our approach is evaluated across 11 languages, and experimental results demonstrate its competitive performance, achieving top-tier results in certain languages."
}