Hi Nat
Imagine it's a translator using MT and somehow he/she has translated
the sentence before and just wants the exact translation. A TM would
solve the problem and Moses surely could emulate the TM but NMT tends
to go overly creative and produces something else.
Then just use a TM for this. Fast and simple.
You can probably create a seq2seq model which will do the copying when
appropriate (see e.g.
https://www.aclweb.org/anthology/P/P16/P16-1154.pdf), but in the
scenario you describe I think there is really no need.
cheers - Barry
On 24/11/16 10:22, Nat Gillin wrote:
Dear Moses Community,
This seems to be prickly topic to discuss but my experiments on a
different kind of data set than WMT or WAT (workshop for asian
translation) has not been able to achieve the stella scores that the
recent advancement in MT has been reporting.
Using state-of-art encoder-attention-decoder framework, just by
running things like lamtram or tensorflow, I'm unable to beat Moses'
scores from sentences that appears both in the train and test data.
Imagine it's a translator using MT and somehow he/she has translated
the sentence before and just wants the exact translation. A TM would
solve the problem and Moses surely could emulate the TM but NMT tends
to go overly creative and produces something else. Although it is
consistent in giving the same output for the same sentence, it's just
unable to regurgitate the sentence that was seen in the training data.
In that matter, Moses does it pretty well.
For sentences that is not in train but in test, NMT does fairly the
same or sometimes better than Moses.
So the question is 'has anyone encounter similar problems?' Is the
solution simply to do a fetch in the train set before translating? Or
a system/output chooser to rerank outputs?
Are there any other ways to resolve such a problem? What could have
happened such that NMT is not "remembering"? (Maybe it needs some
memberberries)
Any tips/hints/discussion on this is much appreciated.
Regards,
Nat
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