Reliable slot and intent detection (SID) is crucial in natural language understanding for applications like digital assistants. Encoder-only transformer models fine-tuned on high-resource languages generally perform well on SID. However, they struggle with dialectal data, where no standardized form exists and training data is scarce and costly to produce. We explore zero-shot transfer learning for SID, focusing on multiple Bavarian dialects, for which we release a new dataset for the Munich dialect. We evaluate models trained on auxiliary tasks in Bavarian, and compare joint multi-task learning with intermediate-task training. We also compare three types of auxiliary tasks: token-level syntactic tasks, named entity recognition (NER), and language modelling. We find that the included auxiliary tasks have a more positive effect on slot filling than intent classification (with NER having the most positive effect), and that intermediate-task training yields more consistent performance gains. Our best-performing approach improves intent classification performance on Bavarian dialects by 5.1 and slot filling F1 by 8.4 percentage points.

本研究针对方言数据匮乏的问题，提出了一种零-shot迁移学习的方法，专注于多个巴伐利亚方言，并发布了慕尼黑方言的新数据集。通过比较不同的辅助任务，研究发现辅助任务对槽位填充的影响更为显著，特别是命名实体识别任务效果最佳。这种方法在巴伐利亚方言的意图分类性能提升5.1个百分点，槽位填充F1提升8.4个百分点，显示出显著的潜在应用价值。

通过辅助任务改善方言槽位和意图检测：多方言巴伐利亚案例研究