Hi Marcin, I have no clean experiments comparing the three, the kbmira paper does compare against online mira and pro, though
"Lattice MIRA emerges as our method of choice. [..] However, all of the MIRA variants perform similarly, though our implementation of online MIRA is an order of magnitude slower, mostly due to its small number of shards." Eva On Fri, Nov 14, 2014 at 12:41 PM, Marcin Junczys-Dowmunt <junc...@amu.edu.pl > wrote: > Hi, > > Eva: And in a sparse-feature scenario compared to PRO or kbmira? > > Barry: Thanks for the pointer. I understand the main problem is > evidence-sparsity for sparse features. I am currently trying to counter > that by using huge devsets (up to 50.000 sentences, divided into pieces of > 5.000, then averaging weights, cross-validation basically) which seems to > help, but I am always suspicious that the optimization method is not doing > as well as it could. So I was hoping you might have something new :) I > remember Collin Cherry talking about lattice Mira, we don't have this in > Moses, have we? > > W dniu 2014-11-14 11:27, Barry Haddow napisał(a): > > Hi Marcin > > I think if you look at the situations where sparse features are > successful, you often find they are tuning with multiple references.This > paper lends support to the idea that multiple references are important: > http://www.statmt.org/wmt14/pdf/W14-3360.pdf. > > cheers - Barry > > On 14/11/14 10:24, Eva Hasler wrote: > > In comparison to MERT? not really, we compared English-French and > German-English at IWSLT 2012 and the baseline scores were a bit higher for > En-Fr a bit lower for De-En. But of course the point is that you can use > more features, so you have to define useful feature sets that are sparse > but still able to generalise On Fri, Nov 14, 2014 at 10:16 AM, Marcin > Junczys-Dowmunt <junc...@amu.edu.pl <mailto:junc...@amu.edu.pl>> wrote: > Speed aside, quality did not improve significantly? W dniu 14.11.2014 o > 11:11, Eva Hasler pisze: > > >
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