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 [1]. > > 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: Links: ------ [1] http://www.statmt.org/wmt14/pdf/W14-3360.pdf
_______________________________________________ Moses-support mailing list Moses-support@mit.edu http://mailman.mit.edu/mailman/listinfo/moses-support
