Frustratingly Simple Few-Shot Slot Tagging

We propose a simple and effective few-shot model for slot tagging. Recent work shows that it is promising to extend standard fewshot classification methods to sequence labeling with CRF-specific augmentations. Such methods show strengths in encoding slot name semantics and slot dependencies. However, we find these strengths can be obtained by a much simpler method, which casts slot tagging into machine reading comprehension (MRC). We fine-tune a standard BERT-based MRC model with a mixture of source domain and (few-shot) target domain data. Such simple method outperforms state-of-the-art methods by a large margin on the SNIPS dataset.