@inproceedings{diaz-etal-2025-late,
title = "{LATE}-{GIL}-nlp at {S}emeval-2025 Task 10: Exploring {LLM}s and transformers for Characterization and extraction of narratives from online news",
author = "Diaz, Ivan and
V{\'a}zquez, Fredin and
Luna, Christian and
Conde, Aldair and
Sierra, Gerardo and
G{\'o}mez - Adorno, Helena and
Bel - Enguix, Gemma",
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://aclanthology.org/2025.semeval-1.92/",
pages = "657--665",
ISBN = "979-8-89176-273-2",
abstract = "This paper tackles SemEval{\textasciitilde}2025 Task{\textasciitilde}10, ``Multilingual Characterization and Extraction of Narratives from Online News,'' focusing on the Ukraine-Russia War and Climate Change domains. Our approach covers three subtasks: (1) {\{}textbf{\{}Entity Framing{\}}{\}}, assigning protagonist-antagonist-innocent roles with a prompt-based Llama{\textasciitilde}3.1{\textasciitilde}(8B) method; (2) {\{}textbf{\{}Narrative Classification{\}}{\}}, a multi-label classification using XLM-RoBERTa-base; and (3) {\{}textbf{\{}Narrative Extraction{\}}{\}}, generating concise, text-grounded explanations via FLAN-T5. Results show a unified multilingual transformer pipeline, combined with targeted preprocessing and fine-tuning, achieves substantial gains over baselines while effectively capturing complex narrative structures despite data imbalance and varied label distributions."
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<abstract>This paper tackles SemEval~2025 Task~10, “Multilingual Characterization and Extraction of Narratives from Online News,” focusing on the Ukraine-Russia War and Climate Change domains. Our approach covers three subtasks: (1) {textbf{Entity Framing}}, assigning protagonist-antagonist-innocent roles with a prompt-based Llama~3.1~(8B) method; (2) {textbf{Narrative Classification}}, a multi-label classification using XLM-RoBERTa-base; and (3) {textbf{Narrative Extraction}}, generating concise, text-grounded explanations via FLAN-T5. Results show a unified multilingual transformer pipeline, combined with targeted preprocessing and fine-tuning, achieves substantial gains over baselines while effectively capturing complex narrative structures despite data imbalance and varied label distributions.</abstract>
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%0 Conference Proceedings
%T LATE-GIL-nlp at Semeval-2025 Task 10: Exploring LLMs and transformers for Characterization and extraction of narratives from online news
%A Diaz, Ivan
%A Vázquez, Fredin
%A Luna, Christian
%A Conde, Aldair
%A Sierra, Gerardo
%A Gómez - Adorno, Helena
%A Bel - Enguix, Gemma
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F diaz-etal-2025-late
%X This paper tackles SemEval~2025 Task~10, “Multilingual Characterization and Extraction of Narratives from Online News,” focusing on the Ukraine-Russia War and Climate Change domains. Our approach covers three subtasks: (1) {textbf{Entity Framing}}, assigning protagonist-antagonist-innocent roles with a prompt-based Llama~3.1~(8B) method; (2) {textbf{Narrative Classification}}, a multi-label classification using XLM-RoBERTa-base; and (3) {textbf{Narrative Extraction}}, generating concise, text-grounded explanations via FLAN-T5. Results show a unified multilingual transformer pipeline, combined with targeted preprocessing and fine-tuning, achieves substantial gains over baselines while effectively capturing complex narrative structures despite data imbalance and varied label distributions.
%U https://aclanthology.org/2025.semeval-1.92/
%P 657-665Markdown (Informal)
[LATE-GIL-nlp at Semeval-2025 Task 10: Exploring LLMs and transformers for Characterization and extraction of narratives from online news](https://aclanthology.org/2025.semeval-1.92/) (Diaz et al., SemEval 2025)
ACL