PAT eam at S em E val-2025 Task 10: Two-stage News Analytical Framework: Target-oriented Semantic Segmentation and Sequence Generation LLM s for Cross-Lingual Entity and Narrative Analysis (2025)

Abstract: This paper presents our approaches for three subtasks in SemEval-2025 Task 10, which focus on entity framing, narrative classification, and narrative extraction in new analysis respectively. We propose a two-stage news analytical framework for both Subtask A and B. In Subtask A (Entity Framing), we design an entity-oriented data processing pipeline to address the issue of redundant information in a news article, and explore effective use of multilingual datasets through sufficient experiments. The system achieves the first place in Bulgarian and the second place in English and Portuguese. In Subtask B (Narrative Classification), a similar narrative-oriented data processing pipeline is adopted to obtain condensed news chunks for each narrative. We conduct in-depth discussion regarding approaches to enhancing both data quality and volume, and explore one-vs-rest classification models and sequence prediction models for multi-label classification tasks. The system ranks first in Bulgarian and second in Russian and Portuguese. In Subtask 3 (Narrative Extraction), we build our system with data augmentation, supervised fine-tuning, and preference-based reinforcement learning. This system achieves the first place in Bulgarian, Russian and Hindi and the second place in Portuguese.

## introduction the rise of the internet and artificial intelligence has revolutionized how we access information, but also leaves us vulnerable to manipulative content and disinformation. various tasks on media analysis have studied entity roles in memes @xcite and persuasion techniques in textual and multimodal datasets @xcite . @xcite @xcite , building on top of these tasks, proposes the challenge of developing cutting-edge nlp systems for multilingual characterization and extraction of narratives from online news. the task aims to automatically identify narratives and the roles of the relevant entities involved. these news analytics capabilities are essential for studying disinformation phenomena on specific targets. the task focuses on three core news analytics challenges in five languages (bulgarian, english, hindi, portuguese, and russian). the original dataset poses two main challenges. firstly, training classification models with entire articles leads to poor performance. we observe that new articles usually contain multiple entities and narratives and, therefore, redundant or interfering information for identifying a specific target. the small number of articles compared to the number of different labels and label power sets is another challenge. in cases where there is not enough supervised data to train a bert-like model, the advantages of leveraging the pre-trained knowledge of llms to solve downstream tasks with a small amount of data become apparent. in summary, this paper presents the following contributions: we present specific approaches and observations for each subtask separately in sections 4 to 6. to address the challenge of low-resource scenarios in nlp tasks, wei and zou propose data augmentation methods including synonym replacement, random insertion, random swap, and random deletion @xcite . advanced strategies namely metadataaware data augmentation, which exchange similar products within a food category @xcite , and prototypical networks, @xcite show promise for data augmentation in professional domains. to take full advantage of the world knowledge within open-source llms, including llama3.3-70b (ai@meta, 2024) and qwen2.5-72b @xcite , we design various prompt to generate multiple sets of training data to mitigate the problem of unbalanced data and overfitting in low-resource scenarios. @xcite discusses multilingual training as another solution to low-resource scenarios of african languages. we perform sufficient experiments on combinations of monolingual datasets in the context this task and identify effective ways to conduct multilingual training. multi-dimensional type-slot label interaction network (mtln) proposed by @xcite is a neural network designed to address multiple nlp tasks using a unified architecture. compared with single-task learning, multi-task learning (mtl) shows enhanced generalization abilities by learning task correlations and complementary features, and mitigates the issue of underfitting @xcite . built upon this idea, we transform the correlation between coarse-grained and fine-grained roles or narratives into a multi-turn dialogue to build multi-task training sets for llms and observe a steady increase. ## conclusion for subtask 1 and 2 in semeval-2025 task 10, we perform semantic segmentation through narrative-or entity-based prompt engineering, obtaining the most relevant contextual information for each narrative or entity to reduce redundant and interfering information in classification. we also analyze the effectiveness of monolingual and multilingual training approaches in subtask 1 and 2, and observe that multilingual datasets composed of adjacent languages achieve better results than monolingual datasets. in subtask 3, we applied data augmentation and achieved strong results using sft fine-tuning combined with preference-optimized reinforcement learning. our systems incorporate various state-of-the-art techniques, including prompt-engineering, lora finetuning, reinforcement learning, and llm for data augmentation, and achieve a total of five first places and five second places in five languages. the results demonstrate the effectiveness of the proposed methods in entity framing, narrative classification, and narrative extraction from news articles. to improve our work in the future, we plan to look deeper into the lexical or label distribution differences between the training set and the test set to help build more robust systems.