100M words/sec is quite impressive, although this approach does not seem to be able to handle very large dictionaries.
If the authors are reading this, it'd be nice to see the actual accuracy on the google analogy dataset (the paper states it is within 1% of the reference implementation) and the performance on the large dataset produced by demo-train-big-model-v1.sh.
Incidentally, at Yahoo we can learn from a dataset of 1066 Billion Words, on a dictionary of 1.42 Billion unique terms, in 7344" (~145M words/sec.)
If the authors are reading this, it'd be nice to see the actual accuracy on the google analogy dataset (the paper states it is within 1% of the reference implementation) and the performance on the large dataset produced by demo-train-big-model-v1.sh.
Incidentally, at Yahoo we can learn from a dataset of 1066 Billion Words, on a dictionary of 1.42 Billion unique terms, in 7344" (~145M words/sec.)