This paper offers a comprehensive review of the research on Natural Language Generation (NLG) over the past two decades, especially in relation to data-to-text generation and text-to-text generation deep learning methods, as well as new applications of NLG technology. This survey aims to (a) give the latest synthesis of deep learning research on the NLG core tasks, as well as the architectures adopted in the field; (b) detail meticulously and comprehensively various NLG tasks and datasets, and draw attention to the challenges in NLG evaluation, focusing on different evaluation methods and their relationships; (c) highlight some future emphasis and relatively recent research issues that arise due to the increasing synergy between NLG and other artificial intelligence areas, such as computer vision, text and computational creativity.
翻译:本文件全面审查了过去二十年来关于自然语言生成的研究,特别是有关数据到文字生成和文本到文字生成的深层学习方法以及新应用自然语言生成技术的研究,调查的目的是:(a) 综合关于自然语言生成核心任务以及实地采用的结构的深层学习研究的最新综述;(b) 仔细和全面地详细介绍各种自然语言生成任务和数据集,并提请注意自然语言生成评估的挑战,重点是不同的评价方法及其关系;(c) 强调由于自然语言生成技术与计算机愿景、文本和计算创造性等其他人工智能领域之间日益增强的协同作用,今后将出现一些重点和较近期的研究问题。