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Imagine that we made word vectors out of PCA reduced sparse tf-idf or countvectorized vectors. I can tell you exactly what each PCA component explains. I could even do that at the word level because it's not difficult to do inverse transforms with some simple dimensionality reduction techniques

The model interpretability goes out the window because we used techniques for the vectorization that kinda suck. NLP is obsessed with self-supervision unnecessarily when they should be innovating in dimensionality reduction techniques




Why do you think NLP practictioners are focusing on self-supervision instead of dimensionality reduction?


I agree, and I have an idea for this dimensionality reduction which makes the original unsupervized word vectors interpretable.

it boggles my mind I haven't seen anyone implement my idea.


SVD has been used for dimensionality reduction of co-occurrence matrices for ages [1], but the resulting word embeddings aren't as performant as those of word2vec/etc. The same is probably true of using PCA.

Word2vec's popularity is the result of people valuing performance (i.e. accuracy) more than interpretability.

[1] https://dl.acm.org/citation.cfm?id=148132


Well, it might be because it's hard to read your mind from here.


no, that wouldn't be mindboggling

what's mindboggling to me is that I haven't seen anyone else come up with the idea independently.


it's so obvious one wouldn't have to read my mind, it's all implicit in the king-man+woman=queen type of relations... if you really ask a second time, fuck it, im not in ML sector, perhaps I just give away the idea...




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