Philipp Koehn wrote: > if the input is provided as a lattice, then it is not > the case that all translations have the same number > of unknown words.
Moreover, the penalty is certainly needed at weight-training time, correct? Then there are different input sentences with different amounts of OOV. The way I anthropomorphize this is that it helps the weights "explain" why some sentences are harder to translate than others. - John Burger MITRE > On Mon, Oct 3, 2011 at 11:28 AM, Kaveh Taghipour > <[email protected]> wrote: >> Hi Christian, >> >> Thanks. You are right. But I think there is no need for such a penalty, >> since all candidates for a given source sentence contain the same number of >> OOVs and so the penalty does not help at all. Do you know the reason? >> >> Cheers, >> Kaveh >> >> On Sun, Oct 2, 2011 at 9:53 PM, Christian Hardmeier <[email protected]> wrote: >>> >>> I think you're on the right track. For some reason, moses doesn't report >>> the OOV penalty feature, which adds a hardcoded penalty of -100 to the total >>> score for each input word that was copied to the output because no suitable >>> translation was found in the phrase table. Your test sentence probably >>> contains two unknown words that account for the -200 difference between the >>> score you calculated and the one output by the decoder. Does that make >>> sense? >>> >>> Best, >>> Christian >>> >>> Kaveh Taghipour <[email protected]> wrote: >>> >>>> Hi, >>>> >>>> I have generated an N-best list with moses, but I do not know how to >>>> compute >>>> the final score. I tried a log-linear model but came up with a wrong >>>> number. >>>> For example: >>>> >>>> moses.ini: >>>> ---------------------------------------- >>>> # distortion (reordering) weight >>>> [weight-d] >>>> 0.120662 >>>> >>>> # language model weights >>>> [weight-l] >>>> 0.251853 >>>> >>>> # translation model weights >>>> [weight-t] >>>> 0.0675335 >>>> 0.103843 >>>> 0.0720954 >>>> 0.00630806 >>>> 0.101934 >>>> >>>> # word penalty >>>> [weight-w] >>>> -0.275771 >>>> >>>> Assuming a log-linear model, the score for the following sentence should >>>> be >>>> *-54.076* but the decoder prints: >>>> >>>> 0 ||| .... ||| d: 0 lm: -219.367 w: -6 tm: -3.17999 -4.43847 -2.63049 >>>> -3.87513 3.99959 ||| *-254.076* >>>> >>>> Thank you in advance for helping. >>>> >>>> Cheers, >>>> Kaveh >>>> >>>> _______________________________________________ >>>> Moses-support mailing list >>>> [email protected] >>>> http://mailman.mit.edu/mailman/listinfo/moses-support >> >> >> _______________________________________________ >> Moses-support mailing list >> [email protected] >> http://mailman.mit.edu/mailman/listinfo/moses-support >> >> > > _______________________________________________ > Moses-support mailing list > [email protected] > http://mailman.mit.edu/mailman/listinfo/moses-support
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