Hi Ondrej, The blow-up is happening in "DecodeStepGeneration::Process(...)", right? If understand the code correctly from a first glance, all possibilities are simply multiplied. And indeed, there seems to be no way to limit the number of combinations in this step. Could something like Cube-Pruning work here to limit the number of options right from the beginning? Best, Marcin
On 10.06.2012 19:02, Ondrej Bojar wrote: > Dear Marcin, > > the short answer is: you need to avoid the blow-up. > > The options that affect pruning during creation of translation options are: > > -ttable-limit ...how many variants of a phrase to read from the phrase > table) > > -max-partial-trans-opt ...how many partial translation options are > considered for a span. This is the critical pruning to contain the > blowup in memory. > > -max-trans-opt-per-coverage ...how many finished options should be then > passed to the search. > -translation-option-threshold ...the same thing, but expressed relative > to the score of the best one. > > If you set the model so that it does blow up but you don't thrash your > machine by setting -max-partial-trans-opt reasonably low, you are very > likely to get a lot of search errors because the pruning of translation > options happens too early, without the linear context of surrounding > translation options. Moses simply does not have good means to handle the > combinatorics of factored models. > > Cheers, Ondrej. > > On 06/10/2012 06:40 PM, Marcin Junczys-Dowmunt wrote: >> 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 _______________________________________________ Moses-support mailing list [email protected] http://mailman.mit.edu/mailman/listinfo/moses-support
