interesting. I don't think you can use MERT because it assumes that there
is a linear relationship between weights and model scores.
However, I think you can use PRO.
I added a feature function
HyperParameterAsWeight
that uses it's weights to change some hyper-parameters such as stack size
and beam width.
It's a hack, but I hope it's a start of what you want to do. Change it
however you want. I haven't tested it so it's up to you to test it
Fyi, the commit is here
https://github.com/moses-smt/mosesdecoder/commit/20e7d078a6ed23e6a293801955ba09e622f993df
On 17 January 2014 16:10, Prashant Mathur <[email protected]> wrote:
> Hi All,
>
> I am tuning the standard models with an extended feature.
> As far as I understood, we can tune only the feature weights in mert.
> In my case I have some more hyper parameters that I want to tune
> together with the feature weights .. for example. feature learning rate.
> Once the nbest-list is created these parameters cannot have any effect
> on the list, but they are useful for generating a better nbest-list.
> Is there a way to tune all the parameters and hyper parameters together?
>
> Thanks,
> Prashant
>
> PS: Till now I was tuning these hyper parameters using Simplex.
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--
Hieu Hoang
Research Associate
University of Edinburgh
http://www.hoang.co.uk/hieu
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