第二语言韩语通用依存树库v1.2：聚焦数据增强与标注方案优化
Second language Korean Universal Dependency treebank v1.2: Focus on data augmentation and annotation scheme refinement
AI总结：
本研究扩充并优化第二语言韩语UD树库，基于该树库微调韩语语言模型，验证了适配性L2数据集对提升二语形态句法分析模型性能的重要作用。
摘要：
我们新增5454条人工标注句子，扩充了第二语言（L2）韩语通用依存（UD）树库。同时修订了标注指南，使其更契合UD框架。基于该增强版树库，我们微调了三个韩语语言模型，并在领域内和领域外的L2韩语数据集上评估其性能。结果显示，微调在多项指标上显著提升了模型性能，凸显了使用适配性强的L2数据集，微调基于母语的通用语言模型以开展L2数据形态句法分析的重要性。
英文摘要：
We expand the second language (L2) Korean Universal Dependencies (UD) treebank with 5,454 manually annotated sentences. The annotation guidelines are also revised to better align with the UD framework. Using this enhanced treebank, we fine-tune three Korean language models and evaluate their performance on in-domain and out-of-domain L2-Korean datasets. The results show that fine-tuning significantly improves their performance across various metrics, thus highlighting the importance of using well-tailored L2 datasets for fine-tuning first-language-based, general-purpose language models for the morphosyntactic analysis of L2 data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
