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Machine learning won't solve natural language understanding
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Re: Machine learning won't solve natural language understanding
#52The history of NLP is littered with people claiming on theoretical grounds that XYZ is unattainable using purely statistical methods, and that some notion of the logical structure of language needs to be brought in. And yet one by one, the XYZ have been attained by statistical methods. If you think there's something NLP can't do using machine learning, make a challenge dataset. That would be much more useful than yet…
I think a lot of people get the impression current NLP models like GPT-3 lack something - "understanding" or something. But they can't say exactly what it is. If they could define exactly what it was, they could indeed describe a way to train for it or program it.
So a theoretical argument could useful if gave us an idea what are our mysterious something it. However, I'm not sure if this article makes a contribution here. Proofs of impossibility generally aren't useful, as you say.
Re: Machine learning won't solve natural language understanding
#53Earlier quoted context omitted.
The two tasks are interconnected. The reasoning flows both ways.
Noise and lack of specificity are completely different problems, and the article concerns itself with the latter
A noisy word might easily be guessed in context. But likewise a semantically ambiguous word might also be guessed due to other factors like the tone of the speaker, facial expressions or more.
I suspect the parent's point is that the disambiguation in both cases might be addressed with information encoded in the other. One can pattern match based on the context. I think some of the work in multimodal transformer models demonstrates this.
Re: Machine learning won't solve natural language understanding
#54Earlier quoted context omitted.
Ironically, exactly these sorts of "statistics can't..." arguments are sort of theoretically bankrupt. Either the thing you want to do is impossible or else a learned model can do it at least almost as well as... idk what the alternative even is, something not learned? Taking this to the extreme, on an example where I have first-hand experience: I would never recommend replacing your compiler passes with transformers…
Are there not situations where a learned model could be insufficient due to lack of real-time learning? It is my understanding that a lot of current applications take a ton of training time / computing power and it's not so trivial to do in the moment. I guess this is less of a theoretical problem and more of a practical problem though.
> I guess this is less of a theoretical problem and more of a practical problem though.
Yeah, exactly. Theoretically, ML can do anything. Practically, not so much.
Re: Machine learning won't solve natural language understanding
#55It's not a given that "thoughts" are representable, let alone in one particular way. I think if you came up with a particular "meaning representation" data structure and defined "understanding" a sentence as mapping it correctly to an instance of that data structure, you could use statistical ML for that. I think that would be pretty interesting, but the sentiment right now seems to be that for a human researcher to creatively devise such a representation for language is pointless, as it will just be inferior to the implicit and unknowable intermediate representation that the computer comes up with during training for a given task. In other words, if you want a translation function from English to German, train that (or train a model that can translate from any language to any language). If you want English to SQL, train that. If some grammatical distinction is relevant for that task, there's no reason in principle that the machine wouldn't learn it.
This approach may indeed have limitations in terms of applying common knowledge and keeping track of state and context in a narrative, across many sentences. I played with GPT-3 for creative writing and found it was not able to keep track of characters and objects and their states very well. For example, we know that if we drop a coin into an empty piggy bank, the coin now resides in the interior of the piggy bank, and the bank will make a noise when shaken, and so on. GPT appears spotty on that stuff. But I also think this is expecting too much of it, as a text prediction engine that sometimes produces a very plausible series of sentences that does take some context into account.
The other point people are discussing is, are we humans doing something fundamentally different when we parse a sentence and find there to be one "correct" parse (along with one or two close-but-clearly-incorrect parses), in our opinion (which we feel confident about and can explain), compared to an AI that will offer a few different "guesses" as possibilities? I think humans are probably doing more cross-checking via different systems, but these systems could be built into the machine model. For example, if we are sure that parse A is correct and parse B is incorrect, and we can explain why, perhaps our confidence comes from our ability to explain, and the machine should be trained to produce logical explanations of its decisions. Or perhaps some of us humans have developed a sort of ambiguity detector, because of the likelihood and cost of certain misunderstandings, which triggers a more careful analysis of the grammatical situation, and the model needs something like that to be more human-like (or more like a careful and conscientious communicator, anyway).
Re: Machine learning won't solve natural language understanding
#56The history of NLP is littered with people claiming on theoretical grounds that XYZ is unattainable using purely statistical methods, and that some notion of the logical structure of language needs to be brought in. And yet one by one, the XYZ have been attained by statistical methods. If you think there's something NLP can't do using machine learning, make a challenge dataset. That would be much more useful than yet…
I'm sad that statistical methods have gained so much ground over more formal and logical methods (edit: maybe I should instead say "that formal and logical methods have lost so much ground compared to statistical methods"), and, while I can see ways to construct examples that the statistical methods ought to have trouble with, I also notice how incredibly well they've done and how many barriers they've blown past in a short period of time.
Recently I was impressed to see that the Winograd schema challenge -- a particular challenge dataset of the kind you mention -- saw huge performance gains from statistical NLP methods, which implies that the statistical methods encoded (something that at least sometimes works a lot like a substitute for) common sense about the world and the context of utterances.
The Winograd schemas were my favorite example of "XYZ" here. They involve pairs of sentences where the sentence is parsed differently based on assumptions from external-world knowledge. For example
Putting the barbell on the glass table isn't a good idea, because it's too (heavy/flimsy).
What is too flimsy?
Or, from memory rather than finding the original version
The local authorities refused a permit to the demonstrators because they (feared/advocated) violence.
Who advocated violence?
The compiler confirmed that that the program was buggy because it (enforced/failed) typechecking.
What failed typechecking?
The doctor recommended surgery for the patient because she was (suffering from/experienced with) coronary artery blockage.
Who was experienced with coronary artery blockage?
In these cases, the most plausible referent of a pronoun changes depending on the semantics of the other word that you fill in, for a reason that has to do with the outside world, not syntax. (I don't each case absolutely has to include a "because" clause, but it's the simplest way to supply disambiguating context.)
I totally thought statistical techniques would be horrible at this. There's good reason to think that these require some detailed knowledge about the world. The statistical techniques were horrible at this, for a while, and now they're abruptly great at it (if I remember correctly, at or above typical human performance).
Re: Machine learning won't solve natural language understanding
#57The history of NLP is littered with people claiming on theoretical grounds that XYZ is unattainable using purely statistical methods, and that some notion of the logical structure of language needs to be brought in. And yet one by one, the XYZ have been attained by statistical methods. If you think there's something NLP can't do using machine learning, make a challenge dataset. That would be much more useful than yet…
I think a lot of people get the impression current NLP models like GPT-3 lack something - "understanding" or something. But they can't say exactly what it is. If they could define exactly what it was, they could indeed describe a way to train for it or program it. So a theoretical argument could useful if gave us an idea what are our mysterious something it. However, I'm not sure if this article makes a contribution…
Also, when people have tried to define exactly what it is, the NLP models have then usually turned out to be able to have or acquire it!
Re: Machine learning won't solve natural language understanding
#58Nobody tell him about GPT-3, I guess…? How do you write this in 2021 and not specifically confront the evidence of what modern ML systems can do and have already done?
I know the author and have worked with him. Even when a system he wrote over many months was dramatically outperformed by a naive machine learning approach that I coded up in an afternoon, it made no difference to his confidence that he was on the right path.
I am genuinely unsure whether this is a good or bad character trait.
Re: Machine learning won't solve natural language understanding
#59The author seems hung up on the idea that because NLU involves apparent "discontinuities" -- places where small variations in interpretation completely transform tasks -- it will not be amenable to smooth, continuous notions like PAC learning, compressibility, and so on. While there's a directional insight there, the terms aren't well defined. And the big story of ML-based NLP in the last decade has been that many ta…
And the big story of ML-based NLP in the last decade has been that many tasks that were presumed too jagged for curve fitting are in fact tractable...
The thing is, I see many situations where large language models have been "impressive" but few situation where they have clearly succeeded in the real world. I'm less than impressed with online translation, Google's AI search is annoying, GPT-3 authored articles are impressive but often senseless, voice interfaces to various corporations are a disaster, etc.
It seems like the main "tractable task" is doing well on the benchmarks themselves. That's not denying there's progress here, it just seems like the "jagged" aspects of language might still be a hurdle.
Re: Machine learning won't solve natural language understanding
#60It's not a given that "thoughts" are representable, let alone in one particular way. I think if you came up with a particular "meaning representation" data structure and defined "understanding" a sentence as mapping it correctly to an instance of that data structure, you could use statistical ML for that. I think that would be pretty interesting, but the sentiment right now seems to be that for a human researcher to…
Something I think about is how computer voice interfaces are changing how we speak[1]. I would not be surprised if the way we talk to each other co-evolves in a way that makes us more easily understood by computers. In other words, it may be easier to teach humans to speak like machines than teach machines to speak like humans. As someone who writes code all day, that's already somewhat the truth.
1. https://www.wsj.com/articles/alexa-dont-let-my-2-year-old-ta...