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A Survey on Retrieval-Augmented Text Generation

2022-02-02 16:18:41
Huayang Li, Yixuan Su, Deng Cai, Yan Wang, Lemao Liu
arXiv_CL
             
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2202.01110

PDF

https://arxiv.org/pdf/2202.01110.pdf

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