Hi Chris,
On 08/24/2012 04:50 PM, Chris Dyer wrote:
> Although forcing monotone alignments sounds like a fairly large
> change, you can use the "parameter" trick in this case because of how
> Giza's HMM estimation works. It alternates between estimating a prior
> distribution over transitions ("jumps") and posterior distributions
> when the sentence pair is observed. Because of how Bayes rule works,
> if your prior assigns 0 probability to some event (in this case, a
> reverse alignment), the posterior distribution will also be 0. For
> this reason, if you initialize carefully, you'll be fine.
thanks for your verbose tips! After browsing the code a few more days,
trying to get help from local SMT-ers and some general black despair i
must admit that i didn't understand what you meant. While what you say
certainly sounds good in theory i haven't even been able to find the
correct piece of code in GIZA that seems to do what you refer to. So I'm
now not only at a loss on how to proceed, but totally lost with my problem.
While i know this is totally bold and way too demanding, may i ask how
good your GIZA++ code understand is, and how long it would take you to
find the rough place of code where these assignments happen and point me
to them? So, well, if that time would be really really short, and you
currently had enough (time) at hand, could i sincerely ask you to help
me with this a tiny little bit? I'm by no means asking you to "hey,
patch me GIZA here", but only a small, a little more precise pointer.
While I'm very happy to get so nice and capable support and help here on
the list I'm at the same time really really sorry that i can't seem to
follow some (read: much) of the conversation and keep up to the level of
knowledge here. Long things short: i feel so dumb right now and I'm
terribly sorry for it. I don't even feel like i should continue bugging you.
Thank you a lot! Really, thanks :P. Regards
- Dario
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