Certain grammatical errors are impossible to fix unless you understand the overall meaning of the text. Sometimes this meaning is embedded over many paragraphs. Errors involving incorrect word usage are unsolvable when words have more than one meaning and you don't comprehend the subject at hand.
We can already "understand the meaning" in a latent space well enough to do machine translation between language pairs the model wasn't trained on, or do additions and subtractions in the latent space of word to vec to suggest they have picked up some semantic meaning from the text.
I don't think this is a problem that requires Strong AI in the vast majority of cases, just very large well groomed corpa and clever engineers.
A non-strong AI can get clues to that meaning (without really understanding anything) based on the words in those previous and subsequent paragraphs, and a huge text corpus.
Personaly I think the usefulness is already to be able to enterpret a concept encoded in slang as the same as the concept derived from a message encoded in a different dialect (or language).
I would never assume a machine spoke this language, only that they understood it. Machines should evolve into speaking succinctly as to not include unneccessary complexity in their messages as they would strive to be well-understood like all other persons do. I fail to see why we would want to produce slang-encoded messages, unless we want to mask the fact we are a machine.
Strong AI isn't a buzzword either, it's been in use for as long as I can remember. Maybe you would be able to understand my Grammer better if I said super human general intelligence and wasted a bunch of space in the process.
I don't think you read my comment? You seem to imply that the corrections would be unambiguous while my point was that some errors are uncorrectable without understanding meaning.
There are some stats from Wordnet on polysemy in English. Obviously this depends on the granularity of a set of senses in a dictionary, but regardless English has many polysemous words (26,000+ according to Wordnet). And more importantly, these polysemous words also tend to be the most common words, hence words like "set" having around 120 definitions in the Oxford English dictionary.
https://wordnet.princeton.edu/wordnet/man/wnstats.7WN.html#s...