@inproceedings{py-etal-2025-duttask10,
    title = "{DUT}task10 at {S}em{E}val-2025 Task 10: {T}hought{F}low: Hierarchical Narrative Classification via Stepwise Prompting",
    author = "Py, Du  and
      Li, Huayang  and
      Yang, Liang  and
      Shaowu, Zhang",
    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-1.59/",
    pages = "424--430",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper describes our system for SemEval-2025 Task 10: Hierarchical Narrative Classification. We propose a two-step hierarchical approach that combines generative reasoning and fine-tuning for sub-narrative classification. The main techniques of our system are: 1) leveraging a large pre-trained model to generate a reasoning process for better context understanding, 2) fine-tuning the model for precise sub-narrative categorization, 3) using a multi-label classification strategy for more accurate sub-narrative identification, and 4) incorporating data augmentation to increase the diversity and robustness of the training data. Our system ranked 1st in Subtask 2 for Hindi, achieving an F1 macro coarse score of 0.56900 and an F1 samples score of 0.53500. The results demonstrate the effectiveness of our approach in classifying narratives and sub-narratives in a multilingual setting, with the additional benefit of enhanced model performance through data augmentation."
}