Year: 2022
Type: article
Abstract: In the last few years, a large number of automatic evaluation metrics have been proposed for evaluating Natural Language Generation (NLG) systems. The rapid development and adoption of such automatic evaluation metrics in a relatively short time has created the need for a survey of these metrics. In this survey, we (i) highlight the challenges in automatically evaluating NLG systems, (ii) propose a coherent taxonomy for organising existing evaluation metrics, (iii) briefly describe different existing metrics, and finally (iv) discuss studies criticising the use of automatic evaluation metrics. We then conclude the article highlighting promising future directions of research.
Source: ACM Computing Surveys
DOI: 10.1145/3485766
Institutions: Indian Institute of Technology Madras, Robert Bosch (India)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3080855795