  Relation Extraction (RE) refers to extracting the relation triples in the
input text. Existing neural work based systems for RE rely heavily on manually
labeled training data, but there are still a lot of domains where sufficient
labeled data does not exist. Inspired by the distance-based few-shot named
entity recognition methods, we put forward the definition of the few-shot RE
task based on the sequence tagging joint extraction approaches, and propose a
few-shot RE framework for the task. Besides, we apply two actual sequence
tagging models to our framework (called Few-shot TPLinker and Few-shot BiTT),
and achieves solid results on two few-shot RE tasks constructed from a public
dataset.
