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.
Abstract (translated)
我们在 SemEval 2025 共享任务 10 的子任务 1（实体框架）中的贡献，旨在解决从较长文档中提供必要段落作为上下文以供带有掩码语言模型进行分类的挑战。我们证明了一个简单的基于实体的上下文选择启发式方法可以利用具有有限上下文窗口的模型来进行文本分类。我们的上下文选择方法和 XLM-RoBERTa 语言模型在性能上可与使用更大生成型语言模型的监督微调方法相媲美，甚至超越后者。
URL
https://arxiv.org/abs/2506.05976
