Moses-support Digest, Vol 110, Issue 22

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Today's Topics:

1. Re: easy steps for beginners (Tom Hoar)
2. Re: MERT's Powell Search (liling tan)


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Message: 1
Date: Fri, 11 Dec 2015 22:41:29 +0700
From: Tom Hoar <tahoar@precisiontranslationtools.com>
Subject: Re: [Moses-support] easy steps for beginners
To: moses-support@mit.edu
Message-ID: <566AEEA9.4060102@precisiontranslationtools.com>
Content-Type: text/plain; charset=windows-1252; format=flowed

Hi Shaimaa,

If you're looking for something more "straightforward for a windows
user," you might want to check-out our Slate Desktop campaign. In
January, we will release a full native Windows SMT application based on
the Moses project. It will include a GUI to train/tune SMT models, a
complete corpus management system and connectors to popular CAT tools.

You can learn more here:

http://igg.me/at/slate-desktop

Regards,
Tom


On 12/11/2015 8:00 PM, moses-support-request@mit.edu wrote:
> Date: Thu, 10 Dec 2015 18:03:43 +0000 (UTC)
> From: Shaimaa Marzouk<marzouk_s@yahoo.de>
> Subject: [Moses-support] easy steps for beginners
> To:<moses-support@mit.edu>
>
> Dear support team,
>
> I would be extremely grateful, if you could help me with the following:
> I have managed to install Moses (which is not an straightforward task for a windows user) and would like to understand, how the system determines the translation output. To simplify the scenario, my plan is to just have 5 sentences as a training data and try to translate 1 sentence. Could you please send me easy instructions / steps for beginners?
>
> BTW, I installed Moses on Ubuntu, use "Fast Align" for Word Alignment. and installed ?IRSTLM? as a language model, but Moses still uses KENLM by default (according to the .ini file)
>
> Thanks a lot:)
>
> Kind regards,
> Shaimaa Marzouk



------------------------------

Message: 2
Date: Fri, 11 Dec 2015 16:45:49 +0100
From: liling tan <alvations@gmail.com>
Subject: Re: [Moses-support] MERT's Powell Search
To: Adam Lopez <alopez@inf.ed.ac.uk>
Cc: moses-support <moses-support@mit.edu>
Message-ID:
<CAKzPaJJK3PTDP+kZt-LOZF_fyWQfPrwjOEJS6=9hfa+X_4Mkkw@mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

Dear Adam and Moses devs/users,

@Adam, Thank you for the explanation on the line 6 of the pseudo code. I
understand it better now.

I have a few more short questions about the pseudo code for the powell
search on slide 37 of http://mt-class.org/jhu/slides/lecture-tuning.pdf,

On line 6 does the "score" in "compute line l: parameter value -> score"
refer to (i) the MT evaluation metric score (e.g. BLEU) between the
translation and the reference sentence or (ii) nbest list weighted overall
score as we see in the last column of a moses generated nbest list (e.g.
http://www.statmt.org/moses/?n=Advanced.Search)?

If it is BLEU, is it true that these sentence-level scores of the nbest
list can be pre-calculated before getting into the powell search?

At line 8 of the pseudo code, when it asks to "find line l with steepest
descent", is it looking for each sentence find the (i) line with the
highest ?j or (i) the line with the highest g(ei|f).

If line 8 of the pseudo code is to find the line with the highest ?k, when
"computing the line l: parameter value -> score", we should also do
something like this ( [g(ei|f) - ?k? 1,...,j-1,j+1,...,|?| ?khk(ei,f) ] /
hj(ei,f) ) to find the line with the highest ?j right?

Then at line 15 of the pseudo code, it says "compute score for value before
first threshold point". Is this "score" different from the "score" at line
6? At line 6, it's a sentence-level score (which I hope it means BLEU and
not the weighted overall score), and at line 15, it seems to be computing
the corpus-level score given the initial parameter values.

If at line 15, it is computing the corpus level score, is it only taking
the best score of the n translations for each reference? And if this is
BLEU, it's doing not a simple case of averaging sentence-level BLEU which
might be kept from line 6, is that right? If it is BLEU, then this score
could be pre-computed before the powell search too, right?

I'm sorry for the many questions and request for clarification. Thanks in
advance for the tips and answers!

Regards,
Liling





On Thu, Dec 10, 2015 at 10:29 AM, Adam Lopez <alopez@inf.ed.ac.uk> wrote:

> Hi Liling ?
>
>
>> We are going through the slides for MT tuning on
>> http://mt-class.org/jhu/slides/lecture-tuning.pdf and we couldn't figure
>> out what does "?ai + bi" on slide 31 refer to.
>>
>> What are the values for "ai" and "bi"? Are they numbers from the nbest
>> list?
>>
>
> [For clarity, I'm going to change the notation slightly here: slide 23
> uses ? to refer to the parameter vector (indexed by i), while slide 31 uses
> it to refer to a single parameter (i.e. an element of this vector). This is
> confusing. Let's use ? as the parameter vector, |?| as its length, and ?j as
> its j-th element, which is what we're optimizing in slides 31-36 (since i
> is already used on slide 31 to index elements of the n-best list, ?i would
> be confusing). I'm also going to use g(ei|f) rather than p(ei|f) since
> this is just a linear model; we aren't doing probabilistic inference here.]
>
> We're going to compute g(ei|f) as a function of a single parameter ?j
> while holding all other parameters fixed. This is just:
>
> g(ei|f) = ?k? 1,...,|?| ?khk(ei,f) = ?jhj(ei,f) + ?k?
> 1,...,j-1,j+1,...,|?| ?khk(ei,f)
>
> Hence ai = hj(ei,f) and bi = ?k? 1,...,j-1,j+1,...,|?| ?khk(ei,f). In
> other words, ai is just the value of the j-th feature on the i-th element
> of the n-best list, and bi is the model score according to all other
> features and weights.
>
> According to the algorithm on slide 37 of
>> http://mt-class.org/jhu/slides/lecture-tuning.pdf, is line 6 where
>> the ?ai + bi computation occurs?
>>
>> compute line l: parameter value ? score
>>
>>
> Yes.
>
> From the nbest list we have lines as such:
>>
>> 0 ||| including options , ????? buy 20 ?????? planes , ??????????? volume
>> - ? ??? 26 ?????????? ???????? . ||| LexicalReordering0= -3.12525 0 0
>> -7.34869 0 0 Distortion0= 0 LM0= -111.207 WordPenalty0= -18 PhrasePenalty0=
>> 17 TranslationModel0= -12.8271 -8.45991 -11.4888 -11.3076 ||| -746.163
>>
>> Let's say we are tuning the first parameter for LexicalReodering0 for
>> this sentence, is it that we only calculate:
>>
>> ? -3.12525 * -746.163
>>
>>
>> Is ai = 3.12525 for this sentence? Is bi = -746.163? What is bi suppose
>> to be?
>>
>
> From the above, b_i is a function of the remaining features and weights;
> so you need to know your current weight vector to compute it.
>
> -A
>
> The University of Edinburgh is a charitable body, registered in
> Scotland, with registration number SC005336.
>
>
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