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Comment by riku_iki | Hacker News Reader

> as is understanding syntax trees and all the interconnections possible inside them so you can NLP the shit out of real world text/speech

But do modern DL approaches (e.g. SQuad, translation models) defy this approach? They train DL models on labeled data without knowing anything about syntax trees, and allow NN do all the magic..

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> They train DL models on labeled data without knowing anything about syntax trees, and allow NN do all the magic.

Sure, but that doesn't mean that knowing about syntax won't improve the result. If you're training a translation model on a huge database of labeled examples, it might discover syntactic relationships from scratch. But if you don't have so much data, you're probably better off using all the auxiliary information you can get.

> But that doesn't mean that knowing about syntax won't improve the result

this is likely correct, additional high quality input will likely improve performance, but creating such input for 200 modern human languages requires significant effort, much larger than allowing NN to solve this problem, that's why researchers invest this effort into NN improvement, not syntax tree creation tooling.

All of us have to strike the right balance between stubbornly believing that NNs will solve all problems in NLP -which, imho, underestimates what linguistics can do for this field of study- and stubbornly saying that only linguistic knowledge will lead to an improvement of the results -which, imho, means underestimating the outstanding work on deep learning techniques done so far. As you said, creating high-quality input is likely to be costly -and I'm not talking about syntax tree creation tooling only here. But, isn't it worth the while? I believe it is.

You could jointly train a NN to parse the sentence and tackle the NLP task you have at the same time, or instead of just using the tokens you could use a parser to add more features too.

edit: clarification

That's how NLP can be a dead end by putting your faith on NN.

So far this dead end outperforms traditional ways though.

Lol for sure any NN-based approach can claim to outperform traditional ways while half of CVPR paper results cannot be reproduced.

CVPR?

Conference on Computer Vision and Pattern Recognition

> while half of CVPR paper results cannot be reproduced.

even if just another half of results are proven to outperform traditional approaches, is it strong enough trend?

You clearly did not see that was sarcastic.

From reading papers on WaveNet and AlphaGo, the problem seems to be incomplete information in the papers, but I am not going to argue with the popular results.

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