Thanks for the papers Barry and Kevin..  
and thanks for the code Hieu. 

Got the ideas for solving the problem. 

Ciao, 
Prashant

On Fri, 2014-01-17 at 15:36 -0600, Kevin Gimpel wrote:
> Also see http://aclweb.org/anthology//W/W12/W12-3159.pdf
> (And thanks for those pointers, Barry.)
> Kevin
> 
> 
> 
> 
> On Fri, Jan 17, 2014 at 3:31 PM, Barry Haddow
> <[email protected]> wrote:
>         Hi Prashant
>         
>         PRO also assumes that the model score depends linearly on the
>         weights, as do the mira variants. There have been some
>         attempts at "tuning" of hyperparameters, but it's a tricky
>         problem. For example, see:
>         
>         http://books.nips.cc/papers/files/nips25/NIPS2012_0650.pdf
>         (for parsing)
>         http://www.aclweb.org/anthology/W13-2229.pdf
>         
>         cheers - Barry
>         
>         
>         
>         On 17/01/14 18:08, Hieu Hoang wrote:
>         
>         > 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.
>         >         _______________________________________________
>         >         Moses-support mailing list
>         >         [email protected]
>         >         http://mailman.mit.edu/mailman/listinfo/moses-support
>         > 
>         > 
>         > 
>         > -- 
>         > Hieu Hoang
>         > Research Associate
>         > University of Edinburgh
>         > http://www.hoang.co.uk/hieu
>         > 
>         > 
>         > 
>         > 
>         > _______________________________________________
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>         > 
>         > 
>         > 
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