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

TL;DR

This paper presents a simple entity-oriented heuristic for selecting context in narrative role classification, enabling effective use of limited-context models like XLM-RoBERTa, achieving competitive results in SemEval 2025.

Contribution

It introduces a straightforward context selection method that improves classification performance with limited context window models, outperforming larger models in some cases.

Findings

Entity-oriented heuristics effectively select context for classification.

Limited context models can match or outperform larger models.

The approach is validated on SemEval 2025 Task 10 data.

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.