Some comments on how different components in the NLG pipeline can “add value” by contributing to the ultimate goal of generating texts that easy for people to read and understand.
I think surface realisation becomes especially challenging when syntax depends on semantics or pragmatics. From engineering perspective, handling phenomena that only occur in a few languages can be painful.
Many students get stressed about their PhD viva (oral exam) even though they are very unlikely to fail. I present some rules and a flowchart to suggest when there is real cause for concern, and when there is not.
A few comments on how I review papers (what I actually do, not what I am supposed to do), and associated advice for authors.
Lexical choice is an area of NLG which really needs machine-learning and data-based techniques.
Perhaps being somewhat idealistic, I think academic researchers should act as “scouts” exploring unknown research terrain
I went to my first developers conference last week and was impressed, not least by the sensible attitude towards deep learning and other trendy AI technology.