Sorry Philipp, I did not ask my question properly.
I was not talking about the phrase table.
I was talking about the language model options that we have. when I said
corpus I was referring to the data for the LM itself.
and in terms of "performance" I was more talking about the impact on
quality.
so
option 1 : 2 LM built from 2 data corpora A and B with 2 weights in
moses.ini
option 2 : 1 LM built from data corpora A+B
option 3 : 2 LM built from corpora A and B and then interpolated into 1
single LM
Hope it's clearer
Le 06/04/2016 16:53, Philipp Koehn a écrit :
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]
<mailto:[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.
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