Hi,
by the way, are there some best-practice decoder settings for heavily 
factored models with combinatorial blow-up? If I am not wrong, most 
settings affect hypothesis recombination later on. Here the heavy work 
happens during the creation of target phrases and future score 
calculation before the actual translation.
Best,
Marcin

W dniu 09.06.2012 16:45, Philipp Koehn pisze:
> Hi,
>
> the idea here was to create a link between the
> words and POS tags early on and use this as
> an additional scoring function. But if you see better
> performance with your setting, please report back.
>
> -phi
>
> On Fri, Jun 8, 2012 at 6:03 PM, Marcin Junczys-Dowmunt
> <[email protected]>  wrote:
>> Hi all,
>> I have a question concerning the "Tutorial for Using Factored Models",
>> section on "Train a morphological analysis and generation model".
>>
>> The following translation factors and generation factors are trained for
>> the given example corpus:
>>
>>      --translation-factors 1-1+3-2 \
>>      --generation-factors 1-2+1,2-0 \
>>      --decoding-steps t0,g0,t1,g1
>>
>> What is the advantage of using the first generation factor 1-2 compared
>> to the configuration below?
>>
>>      --translation-factors 1-1+3-2 \
>>      --generation-factors 1,2-0 \
>>      --decoding-steps t0,t1,g1
>>
>> I understand the 1-2 generation factor maps lemmas to POS+morph
>> information, but the same information is also generated by the 3-2
>> translation factor. Apart from that this generation factor introduces
>> huge combinatorial blow-up, since every lemma can be mapped to basically
>> every possible morphological information seen for this lemma.
>> _______________________________________________
>> Moses-support mailing list
>> [email protected]
>> http://mailman.mit.edu/mailman/listinfo/moses-support
>


-- 
dr inż. Marcin Junczys-Dowmunt
Uniwersytet im. Adama Mickiewicza
Wydział Matematyki i Informatyki
ul. Umultowska 87
61-614 Poznań
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