  Metric-based learning is a well-known family of methods for few-shot
learning, especially in computer vision. Recently, they have been used in many
natural language processing applications but not for slot tagging. In this
paper, we explore metric-based learning methods in the slot tagging task and
propose a novel metric-based learning architecture - Attentive Relational
Network. Our proposed method extends relation networks, making them more
suitable for natural language processing applications in general, by leveraging
pretrained contextual embeddings such as ELMO and BERT and by using attention
mechanism. The results on SNIPS data show that our proposed method outperforms
other state-of-the-art metric-based learning methods.
