论文标题
神经数据之间的创新一代：调查
Innovations in Neural Data-to-text Generation: A Survey
论文作者
论文摘要
在过去十年中引发了自然语言处理（NLP）研究的神经繁荣，同样导致了数据之间的大量创新（DTG）。这项调查提供了对神经DTG范式的合并视图，对方法，基准数据集和评估协议进行了结构化检查。这项调查划出了将DTG与其余自然语言产生（NLG）景观分开的边界，涵盖了文献的最新合成，并突出了更大的NLG伞内外的技术采用阶段。通过这种整体观点，我们重点介绍了DTG研究的有希望的途径，不仅专注于语言能力的系统的设计，而且还集中在表现出公平和问责制的系统上。
The neural boom that has sparked natural language processing (NLP) research through the last decade has similarly led to significant innovations in data-to-text generation (DTG). This survey offers a consolidated view into the neural DTG paradigm with a structured examination of the approaches, benchmark datasets, and evaluation protocols. This survey draws boundaries separating DTG from the rest of the natural language generation (NLG) landscape, encompassing an up-to-date synthesis of the literature, and highlighting the stages of technological adoption from within and outside the greater NLG umbrella. With this holistic view, we highlight promising avenues for DTG research that not only focus on the design of linguistically capable systems but also systems that exhibit fairness and accountability.
