The most talked about NLG application at INLG was product descriptions. Which are very interesting, and also quite different from financial reporting, which is the other application which seems to be taking off.
Many neural NLG systems “hallucinate” non-existent or incorrect content. This is a major problem, since such hallucination is unacceptable in many (most?) NLG use cases. Also BLEU and related metrics do not detect hallucination well, so researchers who rely on such metrics may be misled about the quality of their system.
From a commercial perspective, I think NLG is currently most successful in financial reporting. Although of course there are many great NLG applications in other sectors!
Some thoughts on how to vary words in NLG text. This is aimed at practioners who are building NLG systems, not researchers.
An approach I often take to NLG and indeed AI is to try to understand underying linguistic, NLG, and AI issues, and then to look for simple solutions to these issues.
Some thoughts on language grounding, especially choosing words to express data, and how this depends on context.
Unfortunately I suspect many researchers make their results looks better by using poor baselines. I give some thoughts on this, based on a recent discussion with a PhD student.