3 hours ago
(This post was last modified: 2 hours ago by npcompl33t.)
If they trained the GRU on held-out pages from the entire manuscript, its pretty easy to understand the results. We already know the gradient is the number 1 feature, it dominates PCA1 and MDS1, and would no doubt be the exact thing the GRU matches onto. Masking a lookalike word degrades performance because the 'lookalikeness' of the word is what allows the GRU to locate where on the curve it is and how it should adjust its probabilities. They even say this in their statement:
" In other words, the trained model relies on the earlier lookalike in downstream prediction."
This person should have read your most recent paper about the problems applying static-models to a dynamic-text!
" In other words, the trained model relies on the earlier lookalike in downstream prediction."
This person should have read your most recent paper about the problems applying static-models to a dynamic-text!