期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2021
卷号：2021
页码：1566-1572
DOI：10.18653/v1/2021.eacl-main.134
语种：English
出版社：ACL Anthology
摘要：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.
