Slot and intent detection (SID) is a classic natural language understanding task. Despite this, research has only more recently begun focusing on SID for dialectal and colloquial varieties. Many approaches for low-resource scenarios have not yet been applied to dialectal SID data, or compared to each other on the same datasets. We participate in the VarDial 2025 shared task on slot and intent detection in Norwegian varieties, and compare multiple set-ups: varying the training data (English, Norwegian, or dialectal Norwegian), injecting character-level noise, training on auxiliary tasks, and applying Layer Swapping, a technique in which layers of models fine-tuned on different datasets are assembled into a model. We find noise injection to be beneficial while the effects of auxiliary tasks are mixed. Though some experimentation was required to successfully assemble a model from layers, it worked surprisingly well; a combination of models trained on English and small amounts of dialectal data produced the most robust slot predictions. Our best models achieve 97.6% intent accuracy and 85.6% slot F1 in the shared task.

本研究重点解决了方言和口语变体中的槽位与意图检测（SID）任务中低资源场景的研究缺口。研究提出了一种新颖的方法，通过注入字符级噪声、辅佐任务训练和模型层交换等技术，显著提高了方言SID模型的性能。研究结果显示，噪声注入对模型表现有积极影响，最佳模型在任务中达到了97.6%的意图准确率和85.6%的槽位F1得分。

添加噪声、任务或层？MaiNLP在2025年VarDial共享任务中的挪威方言槽位和意图检测