Context-Sensitive Natural Language Generation: From Knowledge-Driven to Data-Driven Techniques

2014 ◽  
Vol 8 (3) ◽  
pp. 99-115 ◽  
Author(s):  
Nina Dethlefs
Keyword(s):  
Natural Language ◽  
Natural Language Generation ◽  
Data Driven ◽  
Language Generation ◽  
Context Sensitive

scholarly journals Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

2018 ◽  
Vol 61 ◽  
pp. 65-170 ◽  
Author(s):  
Albert Gatt ◽  
Emiel Krahmer
Keyword(s):  
Natural Language ◽  
State Of The Art ◽  
Natural Language Generation ◽  
Data Driven ◽  
Research Topics ◽  
Language Generation ◽  
The Past ◽  
Current State ◽  
Linguistic Input ◽  
New Applications

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past two decades, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of NLP, with an emphasis on different evaluation methods and the relationships between them.

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