What is the objective of natural language generation?

The objective of natural language generation (NLG) is to convert structured data into human-readable language. NLG is a subfield of artificial intelligence that focuses on generating coherent and contextually appropriate language from data sources. This process typically involves analyzing the data and then constructing sentences or narratives that effectively communicate the information contained within the data in a format that is easily understandable to humans.

For example, NLG can take numerical data from databases, graphs, or other structured formats and produce reports, summaries, or even conversational responses in natural language. This capability is valuable in various applications, such as automated report generation, customer support responses, and any scenario where it is beneficial to present data in a user-friendly format.

Other options do not accurately capture the primary goal of NLG. Disruption of language learning and improving machine learning accuracy don't align with NLG's focus on data-to-language conversion. Similarly, while translation is a critical area of natural language processing, it specifically involves translating existing text from one language to another rather than generating original language content from structured data.