I was very impressed by a recent talk about the power of simple white-box models in tasks such as medical diagnosis. I’d love to see more work on simple models in NLP and NLG!

Thge most populat datasets used in summarisation (CNN/DailyMail and XSum) do not actually contain summaries. I find this worrying. Surely the best way to make make progress on summarisation is to use actual summarisation datasets, even if these are less convenient from a “leaderboard” perspective.

Language is diverse, and different syntax, vocabulary, document structures, etc are used in different domains and genres. NLG developers and researchers need to keep this in mind if they are trying to develop generic NLG components.

I am excited by the idea of using a neural language model to improve the output of rule/template NLG. Many academics probably regard this as a boring use of LMs (see my previous blog), but I think it could be very useful in many real world applications.

There is lots of excitement and hype about “gee whiz” uses of language models in NLG, such as generating stories from prompts. However, I suspect there maybe more real-world value in using language models for more mundane tasks such as quality assurance.

We can build much better NLG systems if we understand what users want the systems to do! This may sound trite, but there is very little research in the academic community in understanding user needs and requirements, which is a shame and indeed lost opportunity.