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Computers will be able to handle the ambiguity of human language, transcending their rigid “only do exactly what you tell them” models of the world. So, are re
by joe_the_user 7y ago
Computers will be able to handle the ambiguity of human language, transcending their rigid “only do exactly what you tell them” models of the world.
So, are reasonable examples now of these models allowing semantic context? So, far, what I have seen is generated text where the lack of understanding takes three paragraphs to become obvious rather than one.
Human language is this marvelous framework involving symbols associating with other symbols as well as to well-known and vaguely-guessed facts about the world.
Human relations are very robust and, for example, two people can have a longish conversations where at the end, they realize they're talking about two different people (or different days or events). But in those circumstances, they can correct and adjust. "Solid" understanding is there but it's under a lot of layers of social cues and protocols and multiple meanings.
- mumblemumble 7y ago> So, are reasonable examples now of these models allowing semantic context? This is about where I am stuck. I'll start believing that we truly are on the cusp of a revolution as soon as I see Google Translate reliably knowing when to translate "home" into French as "domicile", "foyer", something those lines, or as "accueil." Right now it seems to very frequently choose "accueil", which is generally wrong, except when you're talking about websites and software user interfaces. That it's biased so strongly toward that error speaks volumes about how critical semantics are to sorting out natural language, and also about how bad current NLP systems are at dealing with semantics.
- joe_the_user 7y agoSyntax and semantics were developed for human language, yet it's much easier to puzzle the difference in a computer language than in human language. With syntax and semantics so wrapped together, however, it kind of seems like you can go a long way with just capturing syntax, rhythm, word choice and etc. Which is to say the semantic side can be even worse than it seems, ie, nonexistent.
- deleted 7y ago[deleted]
- mattkrause 7y agoA long way for what though? The unicorn story is neat, but I don’t have a giant unmet demand for rambling.
- quotemstr 7y ago> This is about where I am stuck. I'll start believing that we truly are on the cusp of a revolution as soon as I see Google Translate reliably knowing when to translate "home" into French as "domicile", "foyer", something those lines, or as "accueil" Isn't that basically the same as the Winograd problem?
- mumblemumble 7y agoI would guess that it's a bit easier. With what I was proposing, you just need to be able to infer the semantics of certain words. With the Winograd problem, you need to grasp the semantics of all the words, and then use that knowledge to infer a pronoun's antecedent based on what yields a more sensical overall interpretation of the sentence.
- perl4ever 7y ago"use that knowledge to infer a pronoun's antecedent based on what yields a more sensical overall interpretation of the sentence" People often assume a very benign, civilized environment for AI. In ordinary life, human beings (on the internet or off) take an adversarial approach to other people modeling the logical framework behind an utterance. They either subvert it for amusement (trolling or comedy) or profit (politics, propaganda, sales), and a tremendous amount of effort goes into it, much of which is very effective. As people have observed, it can be very easy to convince a human that a machine is intelligent, as with ELIZA. But if you violate the presumptions of trust that a machine is designed with, it's going to be surprisingly vulnerable. To be superior to humans, a machine would have to be able to fend off an intelligent person trying to undermine it, not just work when it is spoon-fed.
- elcomet 7y agoI just tried a few examples and it always returned the good word to me. I'm going home -> Je rentre a la maison Home sweet home -> La douceur du foyer (the translation is weird but the word foyer was expected) I feel at home -> Je me sens chez moi (this one is particularly good, it didn't translate the word home directly) Can you share your exemples where it fails?
- mumblemumble 7y agoNot offhand. It's a problem I've noticed more often when looking at translations of larger bodies of text, though. Translating simple, highly standardized sentences like those is what neural translation does best, because it is able to basically just consult an internal phrasebook that it's compiled from its training data.
- NeutronStar 7y agoBack then it was noticeable after a couple words. That we are speaking about paragraphes now speaks for itself. Where will be in in 50 years?