I think that's the easiest way to go for now. I'll give that a try! Thank you very much!
Best, Hubert On Thu, Jul 3, 2014 at 2:45 AM, Philipp Koehn <[email protected]> wrote: > Hi, > > you can also have your feature function read in the word vector mapping table. > > -phi > > On Wed, Jul 2, 2014 at 11:23 AM, Hubert Soyer > <[email protected]> wrote: >> Yes, I am thinking of a new feature function based on word vectors. >> Thank you for your suggestion about the generation step, I'll look into it, >> maybe I'll find a way. >> >> I will also try to create a feature function directly. >> >> Thanks again! >> >> Best, >> >> Hubert >> >> On Jul 2, 2014 11:02 PM, "Philipp Koehn" <[email protected]> wrote: >>> >>> Hi, >>> >>> it would be better to include a word vector obtained by word2vec or other >>> means >>> as a single factor, and generate them with a generation step to avoid >>> filling >>> up the phrase table with redundant information. Unfortunately, there is no >>> source side generation step, which may be a useful addition to the >>> factored >>> model. >>> >>> Of course, the question is what to do with these vectors. I assume that >>> you have >>> a new feature function in mind. >>> >>> -phi >>> >>> On Wed, Jul 2, 2014 at 5:04 AM, Hubert Soyer >>> <[email protected]> wrote: >>> > Hello, >>> > >>> > I have checked the mailing list archive for this question but couldn't >>> > find anything. >>> > I'd be surprised if this question has not been asked yet, if it has, >>> > I'd be happy if you could point me to the corresponding mails. >>> > >>> > Recently, word representations induced by neural networks have gained >>> > a lot of momentum. >>> > Particularly often cited in this context is: >>> > http://code.google.com/p/word2vec/ >>> > >>> > Those vector word representations are vectors that carry some semantic >>> > meaning in them, i.e. semantically similar words have similar vectors >>> > (small distances to each other). >>> > >>> > I have been wondering about the best way to incorporate them in Moses. >>> > >>> > One solution would be to incorporate them as factors in a factored >>> > model: >>> > >>> > http://www.statmt.org/moses/?n=Moses.FactoredTutorial >>> > >>> > It seems to me that I would have to treat each dimension of each word >>> > vector as a separate factor which would lead to a lot of factors. >>> > Usual dimensionalities of those word vectors are 200 or more. >>> > >>> > Is treating each dimension as a factor the best way to incorporate >>> > those vectors or is there anything better I can do? >>> > I don't have to stick to factors, if there is another way. >>> > >>> > Thank you in advance! >>> > >>> > Best, >>> > >>> > Hubert >>> > _______________________________________________ >>> > Moses-support mailing list >>> > [email protected] >>> > http://mailman.mit.edu/mailman/listinfo/moses-support _______________________________________________ Moses-support mailing list [email protected] http://mailman.mit.edu/mailman/listinfo/moses-support
