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.

翻译：本文介绍了我们在2025年 VarDial 研讨会（Scherrer等人，2025）NorSID 共享任务中的提交方案，该任务包含意图检测、槽位填充和方言识别三个子任务，评估数据采用挪威语的不同方言。针对意图检测与槽位填充任务，我们在跨语言场景下对多任务模型进行了微调，以充分利用覆盖17种语言的 xSID 数据集。在方言识别任务中，我们的最终提交模型基于提供的开发集进行微调，该模型在我们的实验中获得了最高评分。测试集上的最终结果表明，与开发集相比，我们的模型性能未出现下降，这很可能源于数据集的领域特定性以及两个子集分布的相似性。最后，我们还对提供的数据集及其特征进行了深入分析，并报告了其他已开展但未取得最佳结果的实验。此外，我们分析了某些方法优于其他方法的原因，重点探讨了训练数据中语言组合与领域特定性对结果的影响。