Few-shot slot tagging becomes appealing for rapid domain transfer and
adaptation, motivated by the tremendous development of conversational dialogue
systems. In this paper, we propose a vector projection network for few-shot
slot tagging, which exploits projections of contextual word embeddings on each
target label vector as word-label similarities. Essentially, this approach is
equivalent to a normalized linear model with an adaptive bias. The contrastive
experiment demonstrates that our proposed vector projection based similarity
metric can significantly surpass other variants. Specifically, in the five-shot
setting on benchmarks SNIPS and NER, our method outperforms the strongest
few-shot learning baseline by $6.30$ and $13.79$ points on F$_1$ score,
respectively. Our code will be released at
this https URL

本文提出了一种基于向量投影网络的少样本检测方法，利用上下文词嵌入在每个目标标签向量上的投影作为单词 - 标签相似性，实现了一种等效的标准化线性模型方法。实验证明，该方法可以明显超越其他变量，在 SNIPS 和 NER 测试集的 5 个样本设置中，我们的方法分别在 F1 得分上比最强的少样本学习基线高出 6.30 和 13.79 分。