Comments (5)
Great question! In our PCFG we have a separate rule for S-->NT where NT is the set of nonterminals (section 2 of the paper), so a full tree in our grammar looks like something like slide 15 of http://www.people.fas.harvard.edu/~yoonkim/data/comp-pcfg-slides.pdf. This is slightly different from the usual CNF for PCFGs.
In practice however, we found that the distribution for S-->NT becomes a one-hot distribution after training, so we can essentially replace one of the nonterminals (which has all the mass) with S. (So in some of my slides I don't show the explicit S-->NT transformation).
Note that we do not count the root-level constituent for evaluation (per convention).
Hope this helps!
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Thanks for the quick reply and the explanation!
from compound-pcfg.
Btw, are there any particular reasons for using S->NT as root rules instead of the usual CNF form S->NT,NT?
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Initially I was experimenting with having several latent vectors for a sentence and was hoping to learn disentangled representations through the three types of rules (i.e. S[z_1]->NT, NT[z_2]->NT NT, NT[z_3]->w). But it turned out having one latent vector was sufficient and S[z]->NT always collapsed to a one-hot distribution. I agree though that having S->NT,NT is cleaner.
from compound-pcfg.
I see, thank you!
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