I was very impressed by a recent paper from a team at Facebook about a production-ready end-to-end neural NLG system. Especially interesting to me was the “engineering” approach to key issues such as accuracy, data collection, and latency.
If we want to deploy AI in the real world, we need to think about “change management” issues. Eg if users think that AI threatens their jobs or adds extra hassle, then uptake will be slow. This has been a problem for AI and statistical algorithms since the 1950s.
When we try to use ML in commercial NLG contexts, one of the challenges is that NLG developers want to be able to customise, configure, and control their systems. So we need ML approaches which do not stop devs from configuring things they are likely to want to change.
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!
