Few-shot Learning for Slot Tagging with Attentive Relational Network
This work addresses slot tagging in NLP for applications like dialogue systems, but it is incremental as it adapts existing metric-based methods to a new task.
The paper tackled few-shot learning for slot tagging by proposing the Attentive Relational Network, which outperformed other state-of-the-art metric-based methods on the SNIPS dataset.
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.