Hi,
this seems to be pretty much what I implemented. What exactly do you
mean by these three lines?:
\STATE count($e|f$) += $\frac{t(e|f)}{\text{s-total}(e)}$
\STATE total($f$) += $\frac{t(e|f)}{\text{s-total}(e)}$
\STATE $t(e|f)$ = $\frac{\text{count}(e|f)}{\text{total}(f)}$
What do you mean by $\frac? The pseudocode I was using shows these
lines as a simple division and this is what my code does. i.e
t(e|f) = count(e|f) / total(f)
In C code something like:
for ( f = 0; f < size_source; f++ )
{
for ( e = 0; e < size_target; e++ )
{
t[f][e] = count[f][e] / total[f];
}
}
Is this the kind of thing you mean?
Thanks
James
Quoting Philipp Koehn <[email protected]>:
> Hi,
>
> I think there was a flaw in some versions of the pseudo code.
> The probabilities certainly need to add up to one. There are
> two normalizations going on in the algorithm: one on the sentence
> level (so the probability of all alignments add up to one) and
> one on the word level.
>
> Here the most recent version:
>
> \REQUIRE set of sentence pairs $(\text{\bf e},\text{\bf f})$
> \ENSURE translation prob. $t(e|f)$
> \STATE initialize $t(e|f)$ uniformly
> \WHILE{not converged}
> \STATE \COMMENT{initialize}
> \STATE count($e|f$) = 0 {\bf for all} $e,f$
> \STATE total($f$) = 0 {\bf for all} $f$
> \FORALL{sentence pairs ({\bf e},{\bf f})}
> \STATE \COMMENT{compute normalization}
> \FORALL{words $e$ in {\bf e}}
> \STATE s-total($e$) = 0
> \FORALL{words $f$ in {\bf f}}
> \STATE s-total($e$) += $t(e|f)$
> \ENDFOR
> \ENDFOR
> \STATE \COMMENT{collect counts}
> \FORALL{words $e$ in {\bf e}}
> \FORALL{words $f$ in {\bf f}}
> \STATE count($e|f$) += $\frac{t(e|f)}{\text{s-total}(e)}$
> \STATE total($f$) += $\frac{t(e|f)}{\text{s-total}(e)}$
> \ENDFOR
> \ENDFOR
> \ENDFOR
> \STATE \COMMENT{estimate probabilities}
> \FORALL{foreign words $f$}
> \FORALL{English words $e$}
> \STATE $t(e|f)$ = $\frac{\text{count}(e|f)}{\text{total}(f)}$
> \ENDFOR
> \ENDFOR
> \ENDWHILE
>
> -phi
>
>
>
> On Sun, Jul 26, 2009 at 5:24 PM, James Read <[email protected]> wrote:
>
>> Hi,
>>
>> I have implemented the EM Model 1 algorithm as outlined in Koehn's
>> lecture notes. I was surprised to find the raw output of the algorithm
>> gives a translation table that given any particular source word the
>> sum of the probabilities of each possible target word is far greater
>> than 1.
>>
>> Is this normal?
>>
>> Thanks
>> James
>>
>> --
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>> Scotland, with registration number SC005336.
>>
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Scotland, with registration number SC005336.
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