Thank you! What do you mean by "mapping is too general"? Would you suggest using the generation for coarse semantic classes (Supersenses)?
Marco On Wed, 29 Jul 2015 1:21 pm Hieu Hoang <[email protected]> wrote: > This is my setup that generates target words from stems > input-factors = word > output-factors = word stem suffix > > alignment-factors = "word -> word" > translation-factors = "word -> stem" > > reordering-factors = "word -> word" > generation-factors = "stem -> word" > decoding-steps = "t0, g0" > you're not likely to get anything useful generating words from brown > clusters - the mapping is just too general > > On 28/07/2015 20:46, Marco Damonte wrote: > > Hi, > > I'm using EMS and factorized translation. For instance, I have this > setting: > > input-factors = word > output-factors = word brown50 > alignment-factors = "word -> word" > translation-factors = "word -> word+brown50" > generation-factors = "brown50" > reordering-factors = "word -> word" > decoding-steps = "t0" > > that is, I have brown clusters as an output factor. > > Does someone can point me the proper way to add a generation step to > create the surface word using the cluster factor? > > I tried: > generation-factors = "brown50 -> word" > but the experiment crashed durinh tuning > > Thanks in advance > Marco > > > > _______________________________________________ > Moses-support mailing > [email protected]http://mailman.mit.edu/mailman/listinfo/moses-support > > > -- > Hieu Hoang > Researcher > New York University, Abu Dhabihttp://www.hoang.co.uk/hieu > >
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