LTG at SemEval-2025 Task 10: Optimizing Context for Classification of Narrative Roles

• URL: http://arxiv.org/abs/2506.05976v1
• Date: Fri, 06 Jun 2025 10:58:45 GMT
• Title: LTG at SemEval-2025 Task 10: Optimizing Context for Classification of Narrative Roles
• Abstract summary: We show that a simple entity-oriented approach for context selection can enable text classification using models with limited context window.<n>Our context selection approach and the XLM-RoBERTa language model is on par with, or outperforms, Supervised Fine-Tuning with larger generative language models.
• Score: 0.3069335774032178
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Our contribution to the SemEval 2025 shared task 10, subtask 1 on entity framing, tackles the challenge of providing the necessary segments from longer documents as context for classification with a masked language model. We show that a simple entity-oriented heuristics for context selection can enable text classification using models with limited context window. Our context selection approach and the XLM-RoBERTa language model is on par with, or outperforms, Supervised Fine-Tuning with larger generative language models.

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