论文标题
一项有关检索的文本生成的调查
A Survey on Retrieval-Augmented Text Generation
论文作者
论文摘要
最近，检索式的文本生成引起了计算语言学界越来越多的关注。与传统的生成模型相比，检索式的文本生成具有显着的优势，尤其是在许多NLP任务中取得了最先进的性能。本文旨在进行有关检索的调查文本生成的调查。首先，它突出显示了检索生成一代的通用范式，然后根据不同的任务来审查著名方法，包括对话响应生成，机器翻译和其他一代任务。最后，它指出了一些最新方法，以促进未来的研究。
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.
