on a related note, you don't even have to use probabilities as features 
in the phrase-table.

for instance, using counts(e|f) and counts(f|e), instead of p(e|f) and 
p(f|e) gives ok translation. The features really are just scores.

using probabilities:
devtest2006: 27.55 BLEU-c ; 28.29 BLEU
nc-dev2007: 22.26 BLEU-c ; 23.46 BLEU
avg: 24.91 BLEU-c ; 25.88 BLEU

using counts:
devtest2006: 27.36 BLEU-c ; 28.11 BLEU
nc-dev2007: 21.64 BLEU-c ; 22.90 BLEU
avg: 24.50 BLEU-c ; 25.51 BLEU


On 20/09/2011 22:14, Miles Osborne wrote:
> exactly,  the only correct way to get real probabilities out would be
> to compute the normalising constant and renormalise the dot products
> for each phrase pair.
>
> remember that this is best thought of as a set of scores, weighted
> such that the relative proportions of each model are balanced
>
> Miles
>
> On 20 September 2011 16:07, Burger, John D.<j...@mitre.org>  wrote:
>> Taylor Rose wrote:
>>
>>> I am looking at pruning phrase tables for the experiment I'm working on.
>>> I'm not sure if it would be a good idea to include the 'penalty' metric
>>> when calculating probability. It is my understanding that multiplying 4
>>> or 5 of the metrics from the phrase table would result in a probability
>>> of the phrase being correct. Is this a good understanding or am I
>>> missing something?
>> I don't think this is correct.  At runtime all the features from the phrase 
>> table and a number of other features, some only available during decoding, 
>> are combined in an inner product with a weight vector to score partial 
>> translations.  I believe it's fair to say that at no point is there an 
>> explicit modeling of "a probability of the phrase being correct", at least 
>> not in isolation from the partially translated sentence.  This is not to say 
>> you couldn't model this yourself, of course.
>>
>> - John Burger
>>   MITRE
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>>
>
>
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