Researchers Develop A Unified Framework For Evaluating Natural Language Generation (NLG)

Natural language generation (NLG) is a broad term that encompasses a variety of tasks that generate fluent text from input data and other contextual information. In actuality, the goals of these jobs are frequently very different. Some well-known instances of NLG include compressing a source article into a brief paragraph conveying the most significant information, converting content presented in one language into another, and creating unique responses to drive the discourse. Natural language processing has advanced at a breakneck pace in terms of enhancing and developing new models for various jobs. However, assessing NLG remains difficult: human judgment is considered the gold standard, but it is typically costly and time-consuming to get.