DUT task10 at S em E val-2025 Task 10: T hought F low: Hierarchical Narrative Classification via Stepwise Prompting (2025)

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.

## introduction the rapid growth of online news content and the ongoing spread of disinformation have made it increasingly important to develop tools that can identify and categorize the underlying narratives shaping public discourse @xcite . to address this, we participated in subtask 2 of semeval-2025 task 10, which focuses on hierarchical narrative classification of multilingual news articles. the goal is to assign both high-level (coarse) and finegrained (sub-narrative) labels to each document based on a predefined two-level taxonomy. to achieve this, we developed a novel hybrid approach that strategically combines the reasoning capabilities @xcite of large language models (llms) with the fine-grained classification power of fine-tuned models. first, we leveraged † both authors contributed equally to this work. * corresponding author. gpt-4o's @xcite api to generate a structured reasoning process (or thought process) for each article. this involved prompting the llm to interpret the text, and identify potential narrative themes before any classification attempt. then, rather than directly feeding the gpt-4o outputs into a classifier, we used the generated reasoning as input for further fine-tuning. we fine-tuned the smaller and more efficient gemma2-9b @xcite model on the reasoning-enriched data, this allowed gemma2-9b to focus on the nuanced task of categorizing sub-narratives more efficiently. moreover, to address the inherent challenges of multi-lingual analysis and to ensure the robustness of our model across the task's five languages (bulgarian, english, hindi, (european) portuguese, and russian), we implemented a comprehensive data augmentation strategy @xcite . this involved techniques designed to increase the diversity of training data, including back-translation and synonym replacement @xcite . various multi-label classification approaches and fine-tuning paradigms were explored to optimize the performance for sub-narrative classification. ## system overview in this section, we detail the methodology developed for semeval-2025 task 10, subtask 2 -a multi-label, multi-class classification task centered on the hierarchical narrative classification of news articles. our system adopts a hybrid framework that integrates generative reasoning with fine-tuned classification. specifically, we leverage gpt-4o to generate structured reasoning chains, which are then used to enrich the input for a smaller, efficient model -gemma2-9b -responsible for sub-narrative prediction. the overall architecture of our system is illustrated in figure 1 . to further enhance performance and robustness, we incorporate comprehensive data preprocessing, augmentation, and multilingual adaptation techniques, addressing the diverse challenges posed by the dataset. ## conclusion by leveraging a series of optimization techniques-including data augmentation, reasoning generation, multi-label loss design, and postprocessing-we developed a robust framework capable of performing multi-label level-2 classification of news texts in a multilingual and crosslinguistic setting. our system achieved first place on the low-resource language hindi, along with competitive results across other languages. in future work, beyond further enriching the training data, we aim to explore language-specific structural features inherent to different language families (e.g., hindi), and to incorporate novel methods and architectures to further enhance model performance, particularly for low-resource languages.