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

arXiv: https://arxiv.org/abs/2506.05976 · HTML: https://mlsanity.com/paper/2506.05976/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 6 Jun 2025.

## Builds on (most cited first, 7 of 7)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://mlsanity.com/paper/2302.00093/) (2023)
- [Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation](https://mlsanity.com/paper/2305.16938/) (2023)
- [Prompt Compression with Context-Aware Sentence Encoding for Fast and Improved LLM Inference](https://mlsanity.com/paper/2409.01227/) (2024)
- [Reducing Distraction in Long-Context Language Models by Focused Learning](https://mlsanity.com/paper/2411.05928/) (2024)
- [UIO at SemEval-2023 Task 12: Multilingual fine-tuning for sentiment classification in low-resource languages](https://mlsanity.com/paper/2304.14189/) (2023)
- [FACT: Examining the Effectiveness of Iterative Context Rewriting for Multi-fact Retrieval](https://mlsanity.com/paper/2410.21012/) (2024)

## Abstract (arXiv)

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.