Related papers: A Survey on Retrieval-Augmented Text Generation

A Survey on Retrieval-Augmented Text Generation

URL: http://arxiv.org/abs/2202.01110v1
Date: Wed, 2 Feb 2022 16:18:41 GMT
Title: A Survey on Retrieval-Augmented Text Generation
Authors: Huayang Li and Yixuan Su and Deng Cai and Yan Wang and Lemao Liu
Abstract summary: Retrieval-augmented text generation has remarkable advantages and has achieved state-of-the-art performance in many NLP tasks. It firstly highlights the generic paradigm of retrieval-augmented generation, and then it reviews notable approaches according to different tasks.
Score: 53.04991859796971
License: http://creativecommons.org/licenses/by/4.0/
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.

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