good to know that the constrained decoding works. And yes, the
reachability of the training data is only theoritical in the absence of
pruning such as cube pruning, beams etc.
On 15/11/2016 20:00, Shuoyang Ding wrote:
Hi Hieu,
I’ve made change 1, 2, 4 before emailing you, and the coverage didn’t
change much. It turns out the bottleneck is on beam-threshold — the
default value was 1e-5, which is a pretty tough limit for constrained
decoding.
After setting that to 0 I played around a little bit with cube-pruning
limit. The coverage is around 25% to 40% depending on what number you
use, but higher coverage comes with longer decoding time, which is
what one would expect to happen.
Still, for string-to-tree constrained decoding the easiest way may
still be decoding with phrase tables built per-sentence, since the
decoding is generally slower. However, even for that, the default
value of beam-threshold needs to be overridden in order to make it
work properly.
Hope the info helps.
Regards,
Shuoyang Ding
Ph.D. Student
Center for Language and Speech Processing
Department of Computer Science
Johns Hopkins University
Hackerman Hall 225A
3400 N. Charles St.
Baltimore, MD 21218
http://cs.jhu.edu/~sding <http://cs.jhu.edu/%7Esding>
On Oct 28, 2016, at 9:27 AM, Hieu Hoang <hieuho...@gmail.com
<mailto:hieuho...@gmail.com>> wrote:
good point. The decoder is set up to translate quickly so there's a
few pruning parameters which throws out low scoring rules or hypotheses.
These are some of the pruning parameters you'll need to change (there
may be more):
1. [feature]
PhraseDictionaryWHATEVER table-limit=0
2. [cube-pruning-pop-limit]
1000000
3. [beam-threshold]
0
4. [stack]
1000000
Make the change 1 at a time in case it makes decoding too slow, even
with constrained decoding.
It may be that you have to run the decoder with phrase-tables that
are trained only on 1 sentence at a time.
I'll be interested in knowing how you get on so let me know how it goes
On 26/10/2016 13:56, Shuoyang Ding wrote:
Hi All,
I’m trying to do syntax-based constrained decoding on the same data
from which I extracted my rules, and I’m getting very low coverage
(~12%). I’m using GHKM rule extraction which in theory should be
able to reconstruct the target translation even only with minimal rules.
Judging from the search graph output, the decoder seems to prune out
rules with very low scores, even if they are the only rule that can
reconstruct the original reference.
I’m curious if there is a way in the current constrained decoding
implementation such that I can disable pruning? Or at least, if it
is feasible to do so?
Thanks!
Regards,
Shuoyang Ding
Ph.D. Student
Center for Language and Speech Processing
Department of Computer Science
Johns Hopkins University
Hackerman Hall 225A
3400 N. Charles St.
Baltimore, MD 21218
http://cs.jhu.edu/~sding <http://cs.jhu.edu/%7Esding>
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