Hi, the number of phrase tables should not matter much, but the number of language models has a significant impact on speed. There are no general hard numbers on this, since it depends on a lot of other settings, but adding a second language model will slow down decoder around 30-50%.
The size of phrase tables and language models matter, too, but not as much, and it seems that in your scenario you are just wondering about splitting up a fixed pool of data. -phi On Wed, Apr 6, 2016 at 6:50 AM, Vincent Nguyen <[email protected]> wrote: > Hi, > > What are (in terms of performance) the difference between the 3 > following solutions : > > 2 corpus, 2 LM, 2 weights calculated at tuning time > > 2 corpus merged into one, 1 LM > > 2 corpus, 2 LM interpolated into 1 LM with tuning > > Will the results be different in the end ? > > thanks. > _______________________________________________ > Moses-support mailing list > [email protected] > http://mailman.mit.edu/mailman/listinfo/moses-support >
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