With binary NNLM, we are able to retain all the gain after interpolation with only 20% of the negative examples.
for the standard NNLM, it means only a subset of the data is seen by the model and it does not change through epochs; for binary NNLM, it means the subset of negative examples for each binary classifier does not change.