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Comment by d--b | Hacker News Reader

Can someone explain what is "unsupervised" about this? I'm guessing this is what confuses me most.

I think this work is interesting, although when you think about it, it's kind of normal that the model converges to a point where there is a neuron that indicates whether the review is positive or negative. There are probably a lot of other traits that can be found in the "features" layer as well.

There are probably neurons that can predict the geographical location of the author, based on the words they use.

There are probably neurons that can predict that the author favors short sentences over long explanations.

But what makes this "unsupervised"?

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It's not labelled data. They didn't tell the model what score is associated with each review. And it learned to predict it anyway. But all it was trained to do was predict the next character.

I wouldn't expect that the neurons are orthogonal on a set of features which we find interesting (sentiment, geographical location). They could be bound up in some other basis of features that we do not find interesting. Other people do not expect this because there are papers about how to incentivize neurons to correspond to interesting features.

>> Other people do not expect this because there are papers about how to incentivize neurons to correspond to interesting features.

Could you clarify that statement? Are you saying that it was unusual for this group to find such a neuron? Also, I did not know that there are papers on how to incentivize neurons to correspond to interesting features. Could you please give me some references on those?

The paper I was thinking of is called: "InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets"[0]. I do not have experience training and investigating neural nets, but from what I read in that paper, there's no reason to presume you'll find neurons that represent a feature you're interested in. In the paper they alter the reward function to get neurons that correspond to the features they are interested in.

[0] https://arxiv.org/pdf/1606.03657v1.pdf

I agree the title is confusing. As far as I understand everything is unsupervised except for the sentiment neuron. The paper itself is actually a better read.

Yes, it's coincidental rather than unsupervised... The fact that to have a good "next character" prediction, you need to know about the "mindset" of the author makes sense. Especially in the context of product reviews.

Had they created a next-move predictor for chess, they wouldn't have been surprised to find a neuron representing the aggressiveness of the player.

It's a good result on its own but the word "unsupervised" is a bit annoying.

They are using the term unsupervised properly, but do I like your characterization of callling it "coincidental". That is basically the essence of unsupervised learning. We have a system doing something (typically some form of optimization), and "coincidentally" it learns something useful that we did not explicitly tell it.

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