Evaluation Metrics: Assessing the quality of NLG outputs

In the field of machine learning, as in the most unrelated fields as well, we need some sort of evaluation. You can think of a student taking an exam, a car in a crash test, a web server on load test, and performance evaluation of a model in AI. Evaluation methods differ among these fields and evolution criteria designed marginally. This procedure is needed mainly to assess the quality of outputs of a model, and also to compare them among different models or with different setups, etc. Natural Language Generation (NLG), a field in Natural Language Processing (NLP), is an applied subfield of artificial intelligence, where the goal is to produce a textual output. It has a vast amount of subtasks like machine translation (MT), question answering (QA), summarization, question generation (QG), etc. Here, the discussion is around the performance of the models whose outputs are text.