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 https://github.com/sz128/few_shot_slot_tagging_and_NER.

中文翻译：

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自然语言理解中用于小样本槽标记的矢量投影网络

在会话对话系统的巨大发展的推动下，少镜头插槽标记对快速域转移和适应变得有吸引力。在本文中，我们提出了一个用于小样本槽标记的向量投影网络，它利用上下文词嵌入在每个目标标签向量上的投影作为词标签相似性。从本质上讲，这种方法等效于具有自适应偏差的归一化线性模型。对比实验表明，我们提出的基于矢量投影的相似性度量可以显着超越其他变体。具体来说，在基准 SNIPS 和 NER 的五次设置中，我们的方法在 F$_1$ 得分上分别比最强的几次学习基线高 6.30 美元和 13.79 美元。我们的代码将在 https://github 上发布。