The fundamental challenges of building useful data-to-text NLG systems are the same regardless of whether we build systems with rules or transformers. We need to understand where NLG is useful, choose good content to communicate, robustly deal with edge cases, allow users to configure and control the system, and evaluate properly. I’d like to see more research on these fundamental issues, regardless of technology used.
One of the highlights of INLG for me was the panel on “What users want from real world NLG”. I summarise a *few* of the really interesting points made about trust, authoring, configurability, human-in-loop, and other key issues for real-world NLG users.
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.
Ehud Reiter's Blog 