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
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