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Today's Topics:
1. Multiple reordering models in moses (Raj Dabre)
2. Re: using sparse features (Barry Haddow)
3. Re: using sparse features (Marcin Junczys-Dowmunt)
----------------------------------------------------------------------
Message: 1
Date: Fri, 14 Nov 2014 18:11:25 +0900
From: Raj Dabre <prajdabre@gmail.com>
Subject: [Moses-support] Multiple reordering models in moses
To: moses-support@mit.edu
Message-ID:
<CAB3gfjD+R11ZXd9+usL-qTAGjdTse5S1Q1gsCKw35dpw5GeiAg@mail.gmail.com>
Content-Type: text/plain; charset="utf-8"
Hello all,
I know that there is a provision for multiple decoding paths which can use
multiple phrase tables.
For example if I have 2 phrase tables for en-fr:
0 T 0
1 T 1
This specifies 2 decoding paths.
What about when I have multiple reordering tables ?
How can I specify something like:
0 R 0
1 R 1
Does my question make sense ?
Or do I just resort to interpolation and merge both reordering tables ?
Any help will be appreciated.
Regards.
--
Raj Dabre.
Research Student,
Graduate School of Informatics,
Kyoto University.
CSE MTech, IITB., 2011-2014
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Message: 2
Date: Fri, 14 Nov 2014 09:39:06 +0000
From: Barry Haddow <bhaddow@staffmail.ed.ac.uk>
Subject: Re: [Moses-support] using sparse features
To: Prashant Mathur <prashant@fbk.eu>, Philipp Koehn
<pkoehn@inf.ed.ac.uk>
Cc: "moses-support@mit.edu" <moses-support@mit.edu>, Hieu Hoang
<hieu.hoang@ed.ac.uk>
Message-ID: <5465CDBA.9030104@staffmail.ed.ac.uk>
Content-Type: text/plain; charset=UTF-8; format=flowed
Hi Prashant
We had to do these kind of dynamic weight updates for online MIRA. The
code is still there, although might have rotted, start by looking at the
weight update methods in StaticData,
cheers - Barry
On 13/11/14 17:05, Prashant Mathur wrote:
> But in CAT scenario we do like this:
>
> translate: sentence 1
> tune: sentence 1 , post-edit 1
> translate: sentence 2
> tune: sentence 2 , post-edit 2
> ...
>
> In this case, I don't have any features generated or tuned before I
> start translating the first sentence.
>
> Old version is complicated, I am coding on the latest version now.
>
> --Prashant
>
>
> On Thu, Nov 13, 2014 at 5:26 PM, Philipp Koehn <pkoehn@inf.ed.ac.uk
> <mailto:pkoehn@inf.ed.ac.uk>> wrote:
>
> Hi,
>
> Typically you want to learn these feature weights when tuning. The
> current setup supports and produces a sparse feature file.
>
> -phi
>
> On Nov 13, 2014 11:18 AM, "Prashant Mathur" <prashant@fbk.eu
> <mailto:prashant@fbk.eu>> wrote:
>
> what if I don't know the feature names before hand?
> In that case, can I set the weights directly during decoding?
>
> On Thu, Nov 13, 2014 at 4:59 PM, Barry Haddow
> <bhaddow@staffmail.ed.ac.uk
> <mailto:bhaddow@staffmail.ed.ac.uk>> wrote:
>
> Hi Prashant
>
> You add something like this to your moses.ini:
>
> [weight-file]
> /path/to/sparse/weights/file
>
> The sparse weights file has the form:
>
> name1 weight1
> name2 weight2
> name3 weight3
> .
> .
> .
>
> At least that's how it works in Moses v2.
>
> cheers - Barry
>
> On 13/11/14 15:42, Prashant Mathur wrote:
>
> Thanks a lot Barry for your answers.
>
> I have another question.
> When I print these sparse features at the end of
> decoding, all sparse features are assigned a weight of
> 0 because all of them were initialized during decoding.
> How can I set these weights for sparse features before
> they are evaluated?
>
>
> Thanks Hieu for the link..
> I am going to update the code as soon as I can.. but
> it will take some time.. will get back to you when I
> do that.
>
> --Prashant
>
>
> On Thu, Nov 13, 2014 at 2:34 PM, Hieu Hoang
> <Hieu.Hoang@ed.ac.uk <mailto:Hieu.Hoang@ed.ac.uk>
> <mailto:Hieu.Hoang@ed.ac.uk
> <mailto:Hieu.Hoang@ed.ac.uk>>> wrote:
>
> re-iterating what Barry said, you should use the
> github moses if
> you want to create your own feature functions,
> especially with
> sparse features. The reasons:
> 1. Adding new feature functions is a pain in v
> 0.91. It's
> trivial now. You can watch my talk to find out why
> http://lectures.ms.mff.cuni.cz/video/recordshow/index/44/184
> 2. It's confusing exactly when the feature
> functions are
> computed. It's clear now (hopefully!)
> 3. I think you had to set special flags
> somewhere to use sparse
> features. Now, all feature functions can use
> sparse features as
> well as dense features
> 4. I don't remember the 0.91 code very well. So
> I can't help you
> if you get stuck
>
>
> On 13 November 2014 11:06, Barry Haddow
> <bhaddow@staffmail.ed.ac.uk
> <mailto:bhaddow@staffmail.ed.ac.uk>
> <mailto:bhaddow@staffmail.ed.ac.uk
> <mailto:bhaddow@staffmail.ed.ac.uk>>>
>
> wrote:
>
> Hi Prashant
>
> I tried to answer your questions inline:
>
>
> On 12/11/14 20:27, Prashant Mathur wrote:
> > Hi All,
> >
> > I have a question about implementing sparse
> feature function.
> > I went through the details on its
> implementation, still
> somethings are
> > not clear.
> > FYI, I am using an old version of moses
> which dates back to
> Release
> > 0.91 I guess. So, I am sorry if my questions
> don't relate to the
> > latest implementation.
>
> This is a bad idea. The FF interface has
> changed a lot since 0.91.
>
> >
> > 1. I was looking at the TargetNgramFeature where
> MakePrefixNgrams adds
> > features in Evaluate function. From the code
> it seems
> MakePrefixNgrams
> > is adding sparse features on the fly. Is it
> correct?
>
> Yes, you can add sparse features on the fly.
> That's really
> what makes
> them sparse features.
>
> >
> > what is the weight assigned to this newly
> added feature? 1 or 0?
>
> The weight comes from the weights file that
> you provide at
> start-up. If
> the feature is not in the weights file, then
> it gets a weight
> of 0.
>
> >
> > 2. What is the difference between these two
> functions?
> >
> > /void PlusEquals(const ScoreProducer*sp,
> const std::string&
> name,
> > float score)/
> > /
> > /
> > /void SparsePlusEquals(const std::string&
> full_name, float
> score)
> > /
>
> In the first, a string from the ScoreProducer
> is prepended to
> the name,
> whilst in the second the string full_name is
> used as the name.
> I think
> we should really use the first form to keep
> features in their own
> namespace, but the second form has pervaded Moses.
>
> >
> > It seems like both of them are used for
> updating sparse feature
> > values.. correct?
> > Or, do the first one points to sparse
> features of a
> particular FF and
> > second one to generic sparse features?
> >
> > 3. How is the structure like if I use one
> StatelessFeatureFunction
> > with unlimited scores? Is it different from
> having unlimited
> sparse
> > features?
> >
> > I assume if there is one FF then there is
> one weight
> assigned to it
> > but in the case of sparse features I have
> one weight for
> each feature.
> FFs can be dense or sparse. What that really
> means is that the
> number of
> feature values for a dense FF is known in
> advance (and so space is
> allocated in the feature value array) but for
> sparse FFs the
> number of
> feature values are not known in advance. So
> even dense FFs can
> have
> several weights associated with them - e.g.
> the phrase table
> features.
> In more recent versions of Moses a given FF
> can have both
> dense and
> sparse values.
>
> >
> > 4. In general when should I compute the
> sparse features?
>
> In general, computing them as soon as you can
> will probably
> make your
> code more efficient. When you are able to
> compute your sparse
> feature
> depends on the feature itself. For example, if
> the feature
> depends on
> only on the phrase pair then it could be
> computed and stored
> in the
> phrase table. This makes the phrase table
> bigger (which could
> slow you
> down) but saves on computation at decoding. On
> the other hand,
> a sparse
> reordering feature has to be mainly computed
> during decoding,
> since we
> do not know the ordering of segments until
> decoding. When I
> implemented
> sparse reordering features though, I
> precomputed the feature
> names since
> you don't want to do string concatenation
> during decoding.
>
>
> cheers - Barry
>
> >
> > Thanks for the patience,
> > --Prashant
> >
> > PS: I am still trying to figure out stuff,
> so questions
> might seem stupid.
> >
> >
> > _______________________________________________
> > Moses-support mailing list
> > Moses-support@mit.edu
> <mailto:Moses-support@mit.edu>
> <mailto:Moses-support@mit.edu
> <mailto:Moses-support@mit.edu>>
> >
> http://mailman.mit.edu/mailman/listinfo/moses-support
>
>
> --
> The University of Edinburgh is a charitable
> body, registered in
> Scotland, with registration number SC005336.
>
> _______________________________________________
> Moses-support mailing list
> Moses-support@mit.edu <mailto:Moses-support@mit.edu>
> <mailto:Moses-support@mit.edu
> <mailto:Moses-support@mit.edu>>
> http://mailman.mit.edu/mailman/listinfo/moses-support
>
>
>
>
> -- Hieu Hoang
> Research Associate
> University of Edinburgh
> http://www.hoang.co.uk/hieu
>
>
>
>
> --
> The University of Edinburgh is a charitable body,
> registered in
> Scotland, with registration number SC005336.
>
>
>
> _______________________________________________
> Moses-support mailing list
> Moses-support@mit.edu <mailto:Moses-support@mit.edu>
> http://mailman.mit.edu/mailman/listinfo/moses-support
>
>
--
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.
------------------------------
Message: 3
Date: Fri, 14 Nov 2014 10:43:55 +0100
From: Marcin Junczys-Dowmunt <junczys@amu.edu.pl>
Subject: Re: [Moses-support] using sparse features
To: moses-support@mit.edu
Message-ID: <5465CEDB.3030303@amu.edu.pl>
Content-Type: text/plain; charset=windows-1252; format=flowed
Apropos MIRA, what's the current best practice tuner for sparse
features? What are you guys using now for say WMT-grade systems?
W dniu 14.11.2014 o 10:39, Barry Haddow pisze:
> Hi Prashant
>
> We had to do these kind of dynamic weight updates for online MIRA. The
> code is still there, although might have rotted, start by looking at the
> weight update methods in StaticData,
>
> cheers - Barry
>
> On 13/11/14 17:05, Prashant Mathur wrote:
>> But in CAT scenario we do like this:
>>
>> translate: sentence 1
>> tune: sentence 1 , post-edit 1
>> translate: sentence 2
>> tune: sentence 2 , post-edit 2
>> ...
>>
>> In this case, I don't have any features generated or tuned before I
>> start translating the first sentence.
>>
>> Old version is complicated, I am coding on the latest version now.
>>
>> --Prashant
>>
>>
>> On Thu, Nov 13, 2014 at 5:26 PM, Philipp Koehn <pkoehn@inf.ed.ac.uk
>> <mailto:pkoehn@inf.ed.ac.uk>> wrote:
>>
>> Hi,
>>
>> Typically you want to learn these feature weights when tuning. The
>> current setup supports and produces a sparse feature file.
>>
>> -phi
>>
>> On Nov 13, 2014 11:18 AM, "Prashant Mathur" <prashant@fbk.eu
>> <mailto:prashant@fbk.eu>> wrote:
>>
>> what if I don't know the feature names before hand?
>> In that case, can I set the weights directly during decoding?
>>
>> On Thu, Nov 13, 2014 at 4:59 PM, Barry Haddow
>> <bhaddow@staffmail.ed.ac.uk
>> <mailto:bhaddow@staffmail.ed.ac.uk>> wrote:
>>
>> Hi Prashant
>>
>> You add something like this to your moses.ini:
>>
>> [weight-file]
>> /path/to/sparse/weights/file
>>
>> The sparse weights file has the form:
>>
>> name1 weight1
>> name2 weight2
>> name3 weight3
>> .
>> .
>> .
>>
>> At least that's how it works in Moses v2.
>>
>> cheers - Barry
>>
>> On 13/11/14 15:42, Prashant Mathur wrote:
>>
>> Thanks a lot Barry for your answers.
>>
>> I have another question.
>> When I print these sparse features at the end of
>> decoding, all sparse features are assigned a weight of
>> 0 because all of them were initialized during decoding.
>> How can I set these weights for sparse features before
>> they are evaluated?
>>
>>
>> Thanks Hieu for the link..
>> I am going to update the code as soon as I can.. but
>> it will take some time.. will get back to you when I
>> do that.
>>
>> --Prashant
>>
>>
>> On Thu, Nov 13, 2014 at 2:34 PM, Hieu Hoang
>> <Hieu.Hoang@ed.ac.uk <mailto:Hieu.Hoang@ed.ac.uk>
>> <mailto:Hieu.Hoang@ed.ac.uk
>> <mailto:Hieu.Hoang@ed.ac.uk>>> wrote:
>>
>> re-iterating what Barry said, you should use the
>> github moses if
>> you want to create your own feature functions,
>> especially with
>> sparse features. The reasons:
>> 1. Adding new feature functions is a pain in v
>> 0.91. It's
>> trivial now. You can watch my talk to find out why
>> http://lectures.ms.mff.cuni.cz/video/recordshow/index/44/184
>> 2. It's confusing exactly when the feature
>> functions are
>> computed. It's clear now (hopefully!)
>> 3. I think you had to set special flags
>> somewhere to use sparse
>> features. Now, all feature functions can use
>> sparse features as
>> well as dense features
>> 4. I don't remember the 0.91 code very well. So
>> I can't help you
>> if you get stuck
>>
>>
>> On 13 November 2014 11:06, Barry Haddow
>> <bhaddow@staffmail.ed.ac.uk
>> <mailto:bhaddow@staffmail.ed.ac.uk>
>> <mailto:bhaddow@staffmail.ed.ac.uk
>> <mailto:bhaddow@staffmail.ed.ac.uk>>>
>>
>> wrote:
>>
>> Hi Prashant
>>
>> I tried to answer your questions inline:
>>
>>
>> On 12/11/14 20:27, Prashant Mathur wrote:
>> > Hi All,
>> >
>> > I have a question about implementing sparse
>> feature function.
>> > I went through the details on its
>> implementation, still
>> somethings are
>> > not clear.
>> > FYI, I am using an old version of moses
>> which dates back to
>> Release
>> > 0.91 I guess. So, I am sorry if my questions
>> don't relate to the
>> > latest implementation.
>>
>> This is a bad idea. The FF interface has
>> changed a lot since 0.91.
>>
>> >
>> > 1. I was looking at the TargetNgramFeature where
>> MakePrefixNgrams adds
>> > features in Evaluate function. From the code
>> it seems
>> MakePrefixNgrams
>> > is adding sparse features on the fly. Is it
>> correct?
>>
>> Yes, you can add sparse features on the fly.
>> That's really
>> what makes
>> them sparse features.
>>
>> >
>> > what is the weight assigned to this newly
>> added feature? 1 or 0?
>>
>> The weight comes from the weights file that
>> you provide at
>> start-up. If
>> the feature is not in the weights file, then
>> it gets a weight
>> of 0.
>>
>> >
>> > 2. What is the difference between these two
>> functions?
>> >
>> > /void PlusEquals(const ScoreProducer*sp,
>> const std::string&
>> name,
>> > float score)/
>> > /
>> > /
>> > /void SparsePlusEquals(const std::string&
>> full_name, float
>> score)
>> > /
>>
>> In the first, a string from the ScoreProducer
>> is prepended to
>> the name,
>> whilst in the second the string full_name is
>> used as the name.
>> I think
>> we should really use the first form to keep
>> features in their own
>> namespace, but the second form has pervaded Moses.
>>
>> >
>> > It seems like both of them are used for
>> updating sparse feature
>> > values.. correct?
>> > Or, do the first one points to sparse
>> features of a
>> particular FF and
>> > second one to generic sparse features?
>> >
>> > 3. How is the structure like if I use one
>> StatelessFeatureFunction
>> > with unlimited scores? Is it different from
>> having unlimited
>> sparse
>> > features?
>> >
>> > I assume if there is one FF then there is
>> one weight
>> assigned to it
>> > but in the case of sparse features I have
>> one weight for
>> each feature.
>> FFs can be dense or sparse. What that really
>> means is that the
>> number of
>> feature values for a dense FF is known in
>> advance (and so space is
>> allocated in the feature value array) but for
>> sparse FFs the
>> number of
>> feature values are not known in advance. So
>> even dense FFs can
>> have
>> several weights associated with them - e.g.
>> the phrase table
>> features.
>> In more recent versions of Moses a given FF
>> can have both
>> dense and
>> sparse values.
>>
>> >
>> > 4. In general when should I compute the
>> sparse features?
>>
>> In general, computing them as soon as you can
>> will probably
>> make your
>> code more efficient. When you are able to
>> compute your sparse
>> feature
>> depends on the feature itself. For example, if
>> the feature
>> depends on
>> only on the phrase pair then it could be
>> computed and stored
>> in the
>> phrase table. This makes the phrase table
>> bigger (which could
>> slow you
>> down) but saves on computation at decoding. On
>> the other hand,
>> a sparse
>> reordering feature has to be mainly computed
>> during decoding,
>> since we
>> do not know the ordering of segments until
>> decoding. When I
>> implemented
>> sparse reordering features though, I
>> precomputed the feature
>> names since
>> you don't want to do string concatenation
>> during decoding.
>>
>>
>> cheers - Barry
>>
>> >
>> > Thanks for the patience,
>> > --Prashant
>> >
>> > PS: I am still trying to figure out stuff,
>> so questions
>> might seem stupid.
>> >
>> >
>> > _______________________________________________
>> > Moses-support mailing list
>> > Moses-support@mit.edu
>> <mailto:Moses-support@mit.edu>
>> <mailto:Moses-support@mit.edu
>> <mailto:Moses-support@mit.edu>>
>> >
>> http://mailman.mit.edu/mailman/listinfo/moses-support
>>
>>
>> --
>> The University of Edinburgh is a charitable
>> body, registered in
>> Scotland, with registration number SC005336.
>>
>> _______________________________________________
>> Moses-support mailing list
>> Moses-support@mit.edu <mailto:Moses-support@mit.edu>
>> <mailto:Moses-support@mit.edu
>> <mailto:Moses-support@mit.edu>>
>> http://mailman.mit.edu/mailman/listinfo/moses-support
>>
>>
>>
>>
>> -- Hieu Hoang
>> Research Associate
>> University of Edinburgh
>> http://www.hoang.co.uk/hieu
>>
>>
>>
>>
>> --
>> The University of Edinburgh is a charitable body,
>> registered in
>> Scotland, with registration number SC005336.
>>
>>
>>
>> _______________________________________________
>> Moses-support mailing list
>> Moses-support@mit.edu <mailto:Moses-support@mit.edu>
>> http://mailman.mit.edu/mailman/listinfo/moses-support
>>
>>
>
------------------------------
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