Can someone explain how to read the graph?
> Figure 2 shows a visualisation of 800 dimensional 1 bit word vectors trained on English Wikipedia (2017). The top 100 closest and furthest word vectors to the target word vector are plotted. Distance is measured by dot product; every 5 word vectors are labelled. A turquoise line separates the 100 closest vectors to the target word from the 100 furthest vectors (labelled “...”). We see that there are qualitative similarities between word vectors whose words are related to each other.
What's happening with figure 1a (epochs vs google accuracy) is that as you train for more epochs the full precision loss continues to decrease (dotted red line) but accuracy also starts decreasing (solid red line). This indicates overfitting (since you'd expect accuracy to increase if loss decreases). The blue lines (quantized training with 1 bit) do not show this which suggests that quantized training seems to act as a form of regularization.
Figure 1b is pretty similar, except on the x axis we have vector dimension. As you increase vector dimension, full precision loss decreases, yet after a certain point full precision accuracy decreases as well. I took this to mean that word2vec training was overfitting with respect to vector dimension.
If you're referring to the image under "Visualizing Quantized Word Vectors" then each row is a word vector (and there are only two colors since each parameter is either -1/3 or +1/3).
I did get that the colors indicated values of dimensions, but I suppose what I really meant is, what is the take-away message? To me, it just looks like noise. Is there a pattern I should look for and go "a-ha, I see"?
What words correspond to these "glitches" for "mushroom" for example? In the case of "mushroom" there is a glitch line just below "earthstar".
Can you provide the full y-axis word vector for any of the visualization charts?
Nice work.
['man', 'woman', 'boy', 'handsome', 'stranger', 'gentleman', 'young', 'drunkard', 'devil', 'lonely', 'lady', 'lad', 'drunken', 'beggar', 'kid', 'effeminate', 'brave', 'bearded', 'himself', 'dressed', 'loner', 'meek', 'sees', 'hustler', 'girl', 'coward', 'thief', 'wicked', 'person', 'balding', 'dashing', 'deranged', 'tramp', 'mysterious', 'him', 'pretends', 'lecherous', 'friend', 'shepherd', 'portly', 'bespectacled', 'jolly', 'thug', 'gangster', 'dapper', 'genius', 'slob', 'beast', 'hero', 'hoodlum', 'policeman', 'elderly', 'drunk', 'manly', 'mustachioed', 'ruffian', 'cop', 'burly', 'beard', 'fool', 'terrified', 'scarecrow', 'scruffy', 'lover', 'peddler', 'remembers', 'supposedly', 'gambler', 'bloke', 'bastard', 'acquaintance', 'mighty', 'playboy', 'unshaven', 'prostitute', 'pimp', 'mans', 'skinny', 'carefree', 'scoundrel', 'crook', 'obsessed', 'surly', 'fancies', 'accosted', 'foolish', 'jovial', 'cocky', 'shifty', 'loves', 'narrator', 'butler', 'dying', 'casually', 'waiter', 'evil', 'frightened', 'gigolo', 'conman', 'cunning']
(Edit: Actually this isn't quite right as it doesn't match the image. Many of these vectors actually have the same distance to "man" and the dict doesn't keep a deterministic order. What you can do is modify https://github.com/agnusmaximus/Word2Bits/blob/development/s... and run it on the 1 bit 400k vectors and see what it prints out. To run it do: `python w2bvisualize.py path_to_1bit800d400kvectors` then it should generate similar figures as in the writeup)