  Recently, retrieval-augmented text generation attracted increasing attention
of the computational linguistics community. Compared with conventional
generation models, retrieval-augmented text generation has remarkable
advantages and particularly has achieved state-of-the-art performance in many
NLP tasks. This paper aims to conduct a survey about retrieval-augmented text
generation. It firstly highlights the generic paradigm of retrieval-augmented
generation, and then it reviews notable approaches according to different tasks
including dialogue response generation, machine translation, and other
generation tasks. Finally, it points out some important directions on top of
recent methods to facilitate future research.
