用辅助任务提升德语巴伐利亚方言的意图与槽位识别效果
Improving Dialectal Slot and Intent Detection with Auxiliary Tasks: A Multi-Dialectal Bavarian Case Study
- 设计三种辅助任务，通过中间任务训练提升模型性能
- 在慕尼黑方言上实现意图识别提升5.1%，槽位填充F1提升8.4%
- 命名实体识别对槽位识别帮助最大，适合低资源方言场景
可靠的槽位和意图识别（SID）在数字助手等自然语言理解应用中至关重要。基于高资源语言微调的编码器型Transformer模型在标准语言上表现良好，但在无标准化形式、标注数据稀缺且昂贵的方言数据上表现不佳。本文聚焦多巴伐利亚方言的零样本迁移学习，发布了一个慕尼黑方言的新数据集。评估了在巴伐利亚语中训练的辅助任务模型，并比较联合多任务学习与中间任务训练的效果。对比了三种辅助任务：词粒度句法任务、命名实体识别（NER）和语言建模。结果表明，辅助任务对槽位填充的提升优于意图分类（其中NER效果最佳），且中间任务训练带来更一致的性能提升。最优方法使巴伐利亚方言上的意图分类准确率提升5.1个百分点，槽位填充F1提升8.4个百分点。
原文摘要 · Abstract (English)
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.
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