LTG at SemEval-2025 Task 10: Optimizing Context for Classification of Narrative Roles
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916782321696768 |
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| author | Rønningstad, Egil Negi, Gaurav |
| author_facet | Rønningstad, Egil Negi, Gaurav |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05976 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LTG at SemEval-2025 Task 10: Optimizing Context for Classification of Narrative Roles Rønningstad, Egil Negi, Gaurav Computation and Language 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. |
| title | LTG at SemEval-2025 Task 10: Optimizing Context for Classification of Narrative Roles |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2506.05976 |