LTG at SemEval-2025 Task 10: Optimizing Context for Classification of Narrative Roles
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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.
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References · 7
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
- 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 820
- 2023Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluationcited by 134
- 2024Prompt Compression with Context-Aware Sentence Encoding for Fast and Improved LLM Inferencecited by 51
- 2024Reducing Distraction in Long-Context Language Models by Focused Learningcited by 8
- 2023UIO at SemEval-2023 Task 12: Multilingual fine-tuning for sentiment classification in low-resource languagescited by 2
- 2024FACT: Examining the Effectiveness of Iterative Context Rewriting for Multi-fact Retrievalcited by 2
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