Deep Learning for Natural Language Processing – ICLR 2017 Discoveries
amundtveit.com
amundtveit.com
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.
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...
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.
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.
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.
http://smerity.com/articles/2016/iclr_2017_submissions.html
I'd also strongly recommend people read through all the submissions themselves as every person seems to select a different set of papers depending on what they're interested in. My write-up has very few papers discussing CNNs for example as I'm focused on RNNs for the most part.