用多语言迁移和自动标注解决挪威语方言数据稀缺问题
HiTZ at VarDial 2025 NorSID: Overcoming Data Scarcity with Language Transfer and Automatic Data Annotation
- 跨语言微调多任务模型，利用17种语言的xSID数据集
- 方言识别模型在测试集表现与开发集持平，未出现性能下降
- 实证分析表明语言组合与领域一致性对效果影响显著
本文提交至2025年VarDial研讨会的NorSID共享任务，包含意图识别、槽位填充和方言识别三项任务，评估对象为不同挪威语方言的数据。针对意图识别与槽位填充，我们在跨语言设置下微调了多任务模型，利用17种语言的xSID数据集进行训练。对于方言识别，最终提交模型在提供的开发集上微调，取得了实验中最高分。测试集结果表明，模型性能未低于开发集，可能由于数据集具有领域特异性且两子集分布相似。此外，我们深入分析了所提供数据集及其特征，并报告了若干未达最优的实验。最后，我们探讨了部分方法更成功的原因，主要归因于语言组合与训练数据领域一致性的影响。
原文摘要 · Abstract (English)
In this paper we present our submission for the NorSID Shared Task as part of the 2025 VarDial Workshop (Scherrer et al., 2025), consisting of three tasks: Intent Detection, Slot Filling and Dialect Identification, evaluated using data in different dialects of the Norwegian language. For Intent Detection and Slot Filling, we have fine-tuned a multitask model in a cross-lingual setting, to leverage the xSID dataset available in 17 languages. In the case of Dialect Identification, our final submission consists of a model fine-tuned on the provided development set, which has obtained the highest scores within our experiments. Our final results on the test set show that our models do not drop in performance compared to the development set, likely due to the domain-specificity of the dataset and the similar distribution of both subsets. Finally, we also report an in-depth analysis of the provided datasets and their artifacts, as well as other sets of experiments that have been carried out but did not yield the best results. Additionally, we present an analysis on the reasons why some methods have been more successful than others; mainly the impact of the combination of languages and domain-specificity of the training data on the results.
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