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by nl 3505 days ago
A few brief notes:

Not at all sure the recommendations system papers should be grouped with QA papers. The techniques used aren't that closely related.

FastText(.zip) continues to be a weird project.

LEARNING A NATURAL LANGUAGE INTERFACE WITH NEURAL PROGRAMMER - yeah, that's something. The neural programmer and related stuff seems like something out of sci-fi, even more so than normal neural networks.
2 comments
deet 3505 days ago
The neural programmer is indeed interesting. The current research into have neural networks learn algorithms and procedures for data retrieval and computation is really exciting (to me at least). It almost makes the image classification work of a few years ago seem quaint.

For those who don't want to read the paper, it's a way to (try to) have a computer learn to answer questions on datasets, like those contained here: http://nlp.stanford.edu/software/sempre/wikitable/viewer/#20...

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Eridrus 3505 days ago
> FastText(.zip) continues to be a weird project.

What is weird to you about the project? I haven't looked at the details, but the motivation seems pretty obviously to be able to run deep learning models on people's phones without seriously impacting UX.

Hell, even running a large vocabulary model on a server can be annoying when these models take ~10GB to just store the word vectors.

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nl 3505 days ago
Well, it isn't deep learning for one thing.

Basically it's a reappraisal of early 2000-style manually engineered features. It's good work, but doesn't add much over VopalWabbit.

I haven't read the .zip paper in depth, but the mobile angle doesn't seem convincing to me. Text models generally just aren't that big! Drop the number of dimensions in W2V and it's really pretty small, and still expressive.

Don't get me wrong - I like FastText. But it suprises me it remains a research direction - almost everyone else is working on trying other approaches to get an AlexNet like breakthrough on NLP tasks. It's pretty clear that breakthrough won't come from the FastText approach.

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Eridrus 3505 days ago
> Well, it isn't deep learning for one thing.

You know, I didn't actually realise that. I had only glanced at it and assumed they were applying these ideas to deep models.

> Drop the number of dimensions in W2V and it's really pretty small, and still expressive.

I don't think it's crazy to want to be able to do get better performance with small memory targets though.

They're working on other directions too, but maybe this is useful for their product groups.

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choxi 3505 days ago
What do you think are the more promising approaches? If you could link to some papers, I'd love to read some of them.
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