Machine learning won't solve natural language understanding
thegradient.pub
Machine learning won't solve natural language understanding
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Re: Machine learning won't solve natural language understanding
#2An armchair effort to redefine the goalposts and judge NLP, but proof is in the pudding. For now, large language models are the best flavor. NLP models are already useful even in this early stage.
Re: Machine learning won't solve natural language understanding
#3An armchair effort to redefine the goalposts and judge NLP, but proof is in the pudding. For now, large language models are the best flavor. NLP models are already useful even in this early stage.
I look forward to when something like Siri or Google Assistant is hooked up to GPT-3. The current voice assistant ML systems are useless for anything but the most basic of tasks.
Re: Machine learning won't solve natural language understanding
#4An armchair effort to redefine the goalposts and judge NLP, but proof is in the pudding. For now, large language models are the best flavor. NLP models are already useful even in this early stage.
They are certainly useful, but it seems quite plausible that they could continue being useful without ever "solving" the problem, in the general sense.
Re: Machine learning won't solve natural language understanding
#5> In other words, we must get, from a multitude of possible interpretations of the above question, the one and only one meaning that, according to our commonsense knowledge of the world, is the one thought behind the question some speaker intended to ask.
But, can humans do this? I think not; I still disagree with the author about what "Do we have a retired BBC reporter that was based in an East European country during the Cold War?", translated into code, means.
They write "Doing the correct quantifier scoping: we are looking not for ‘a’ (single) reporter who worked in ‘some’ East European country, but to any reporter that worked in any East European country"
My interpretation of this requirement is that they want a list of all the reporters who meet the criteria. However, I would probably write this query to return a boolean, not a list of reporters.
And even if my interpretation is wrong... well, my point is still correct, because I failed to extract the "the one and only one meaning" that the author intended from that sentence.
Even humans are only probably approximately correct.
Re: Machine learning won't solve natural language understanding
#6> I have discussed in this article three reasons that proves Machine Learning and Data-Driven approaches are not even relevant to NLU
This is a pretty hard-line position to take, and given the authors credentials I'm inclined to believe this is somehow poorly worded and not reflective of the thesis he intended with this article.
> Languages are the external artifacts that we use to encode the infinite number of thoughts that we might have.
> in building larger and larger language models, Machine Learning and Data-Driven approaches are trying to chase infinity in futile attempt at trying to find something that is not even ‘there’ in the data
> Ordinary spoken language, we must realize, is not just linguistic data
I would be curious to know what the author thinks of multimodal representation learning - which is conceptually promising in that it opens the door for machine learning models to learn relationships that span text, images, video, etc. For example OpenAI's CLIP [1], and other models like it.
Re: Machine learning won't solve natural language understanding
#7An armchair effort to redefine the goalposts and judge NLP, but proof is in the pudding. For now, large language models are the best flavor. NLP models are already useful even in this early stage.
> An armchair effort
Given the authors credentials and publication history [1] it's a bit disingenuous to call this an 'armchair effort'.
[1] https://scholar.google.com/citations?user=i5sEc1YAAAAJ&hl=en...
Re: Machine learning won't solve natural language understanding
#8> In other words, we must get, from a multitude of possible interpretations of the above question, the one and only one meaning that, according to our commonsense knowledge of the world, is the one thought behind the question some speaker intended to ask. But, can humans do this? I think not; I still disagree with the author about what "Do we have a retired BBC reporter that was based in an East European country duri…
I’m confused, but think I concur with you.
The two phrasings read as identical to me:
- do we have a reporter who worked in some East European country?
- is there any reporter (among ours) who worked in any East European country?
- exists? (reporter in our-reporters) where (reporter.base in east-european-countries)
I’m confused where the difference is supposed to be, between them.
For every case, naming a person for whom that is true is a witness “yes” is correct; and being unable to is “no” being correct.
Re: Machine learning won't solve natural language understanding
#9An armchair effort to redefine the goalposts and judge NLP, but proof is in the pudding. For now, large language models are the best flavor. NLP models are already useful even in this early stage.
> An armchair effort Given the authors credentials and publication history [1] it's a bit disingenuous to call this an 'armchair effort'. [1] https://scholar.google.com/citations?user=i5sEc1YAAAAJ&hl=en...
That's not a particularly impressive publication record.
Re: Machine learning won't solve natural language understanding
#10> In other words, we must get, from a multitude of possible interpretations of the above question, the one and only one meaning that, according to our commonsense knowledge of the world, is the one thought behind the question some speaker intended to ask. But, can humans do this? I think not; I still disagree with the author about what "Do we have a retired BBC reporter that was based in an East European country duri…
> Even humans are only probably approximately correct.
This is very true, more true than we realize. Notice how much more "could you repeat that?" we have with masks on. It's not JUST the mild muffling of the speaker's voice, it's not seeing their lips move. We're all lip readers to a small degree, and it helps inform our decoding to see the lips. Fff and th sound similar but look very different.
Even without that, think if your life, ever said "Waht was that? Oh, right..." then reply. At first you missed part of what they said (for various reasons), but you were able to "interpolate" the missing part from the context, and most of the time you get it right.
Our communication modes are lossy, and our brains make up for that to a large degree. That's the hole in natural language decoding, figuring out the hinting needed for an engine, because we're not totally aware of how we do it ourselves.