@inproceedings{li-etal-2025-emo,
    title = "{EMO}-{NLP} at {S}em{E}val-2025 Task 11: Multi-label Emotion Detection in Multiple Languages Based on {XLMCNN}",
    author = "Li, Jing  and
      Xian, Yucheng  and
      Yang, Xutao",
    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/mtsummit-25-ingestion/2025.semeval-1.110/",
    pages = "801--806",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper describes the system implemented by the EMO-NLP team for track A of task 11 in SemEval-2025: Bridging the Gap in Text-Based Emotion Detection. The task focuses on multiple datasets covering 28 languages for multi-label emotion detection. Most of these languages are low-resource languages. To achieve this goal, we propose a multilingual multi-label emotion detection system called XLMCNN, which can perform multi-label emotion detection across multiple languages. To enable emotion detection in various languages, we utilize the pre-trained model XLM-RoberTa-large to obtain embeddings for the text in different languages. Subsequently, we apply a two-dimensional convolutional operation to the embeddings to extract text features, thereby enhancing the accuracy of multi-label emotion detection. Additionally, we assign weights to different emotion labels to mitigate the impact of uneven label distribution. In this task, we focus on nine languages, among which the Amharic language achieves the best performance with our system, ranking 21st out of 45 teams."
}