  Relation extraction is an important but challenging task that aims to extract
all hidden relational facts from the text. With the development of deep
language models, relation extraction methods have achieved good performance on
various benchmarks. However, we observe two shortcomings of previous methods:
first, there is no unified framework that works well under various relation
extraction settings; second, effectively utilizing external knowledge as
background information is absent. In this work, we propose a knowledge-enhanced
generative model to mitigate these two issues. Our generative model is a
unified framework to sequentially generate relational triplets under various
relation extraction settings and explicitly utilizes relevant knowledge from
Knowledge Graph (KG) to resolve ambiguities. Our model achieves superior
performance on multiple benchmarks and settings, including WebNLG, NYT10, and
TACRED.
