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The way I see this working is by adding a second layer of machine learning to the process. The first layer would work as usual (associate words with utterance-i

The way I see this working is by adding a second layer of machine learning to the process. The first layer would work as usual (associate words with utterance-independent meanings), but the second layer would compare those meanings with the utterance in question, shave most (or all) of them out, and either add a new meaning or reinforce an old one. But again, I'm no programmer. I might be saying something that is already done, or unfeasible for some reason.

shoo 5y ago
(this is also not my field) I was thinking about this a little more: In some cases (such as your excellent wug example) it might be possible to heuristically associate the word "wug" to a probable contextual meaning "your cat" from a shortlist of candidate objects ("you (conversing)", "me (also conversing)", "cat", "bed") without understanding that cats are things that sleep on beds, and some kind of educated guess that "that wug" doesn't refer to the speaker themselves! In other cases, figuring this out might require a fair bit of side knowledge about the meanings of what is being discussed, and how they relate to each other, to almost logically deduce what entity is being labelled, given knowledge of how those entities work. That might be dramatically harder to get get a machine to do well. Perhaps a machine being able to to this would be a leap forward in machine intelligence from today's ML "stochastic parrots".