Thanks for sharing! A few things stood out to me:
parsing English is taking the words in some utterance and turning them into a form a computer can understand.
I know you said this is a “sweeping generalization”, but this seems a bit generous, since the computer isn’t really “understanding” the utterance. Some semantic parsers do go this far, in a limited sense, such as text-to-SQL parsers (e.g., “which students passed the exam?” ->
SELECT s.id FROM students AS s JOIN examscores AS e ON s.id = e.studentid WHERE e.score >= 60, assuming the computer understands the SQL statement) but their applicability is extremely narrow. Maybe “…a form a computer can reason about” or “…deal with”?
Also you seem to be conflating or glossing over syntactic vs semantic parsing, which is probably fine for something informal like a blog post. In some frameworks, like how HPSG is used in DELPH-IN, these happen simultaneously. Others may do it as a transformation of the syntax or by ignoring syntax and going straight to some meaning representation.
We still haven’t fully figured it out.
I don’t think that’s a plausible goal, even for one language. Never mind that there are many varieties or dialects and that these evolve over time, but the idea that there is something there to “figure out”, such that all linguists would be in agreement of its veracity, is dubious. Rather, we are in the business of modeling language (at least the bits that are important to us), and, as the aphorism goes, all models are wrong, but some are useful.
Regarding (Neo) Davidsonian semantics, my understanding is that Donald Davidson’s version, dubbed “Davidsonian semantics”, introduced the event variable but that verbs are still like frames in that they have fixed places for their arguments (e.g., stab(e, Brutus, Caesar)), but Neo-Davidsonian (by Terry Parsons) decoupled arguments from their verbs (e.g., stab(e) ^ AGENT(e, Brutus) ^ PATIENT(e, Caesar)). This made it easier to attribute other information that may not be part of some fixed frame (e.g., INSTRUMENT(e, knife), etc.). Under this view, MRS is more like Davidsonian semantics, although there’s RMRS and possibly the more recent DMRS which are more like Neo-Davidsonian semantics.
BTW, I’ve always thought the closed world of a game would be a good testbed for actually applying our semantic representations in natural language understanding or generation tasks. I’m interested to see what you come up with.