We’ve just completed a shared task on evaluating accuracy of NLG texts. This was really interesting, and amongst other things showed that current neural data-to-text systems struggle to learn how to use some words which have clear but relatively complex definitions.
There is a military saying that “amateurs discuss tactics, professionals discuss logistics”. Similarly I think AI professionals should focus on data more than models. I suggest four simple initial questions to ask about your data if you want to build an ML system.
Some thoughts on key NLG challenges in explainable AI: evaluation, conceptual alignment, narrative. Comments are welcome!
Lexical choice is an area of NLG which really needs machine-learning and data-based techniques.