Moses-support Digest, Vol 120, Issue 7

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Today's Topics:

1. two open positions at the University of Munich (LMU)
(Alexander Fraser)
2. Re: Age feature (Michael Denkowski)


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Message: 1
Date: Wed, 5 Oct 2016 20:52:02 +0200
From: Alexander Fraser <fraser@cis.uni-muenchen.de>
Subject: [Moses-support] two open positions at the University of
Munich (LMU)
To: moses-support <moses-support@mit.edu>, corpora@uib.no,
mt-list@eamt.org
Message-ID:
<CADaL4v3xBYGq22YMdAXLQPNmq+ZVc4Nuv1SuAyQf1GUU+GUgEA@mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

Open Positions at the University of Munich

I am expanding my research group, which is located at the Center for
Information and Language Processing at the University of Munich (LMU).

Two positions are available on my European Research Council Starting
Grant. One position is for a post-doc. The other position is for a
researcher interested in doing a PhD.

Both of these positions will focus on cutting edge research into using
deep learning techniques to solve machine translation (neural machine
translation). The research group is a well-known machine translation
research group with strong interests in related natural language
problems.

There will be possibilities to teach, supervise bachelors/masters
students, and to collaborate on grant applications. Post-Docs may also
participate in the supervision of doctoral students. The working
language of the group is English, no knowledge of German is required.

There is significant interaction with the research groups of Hinrich
Sch?tze (deep learning and a variety of NLP problems) and Helmut
Schmid (neural machine translation, parsing, tagging, morphology). We
also have several active collaborations with colleagues at other
universities.

The University of Munich is one of the top research universities in
Europe. It was the highest ranked institution on several measures in
the Exzellenzinitiative funding program organized by the German
national science foundation in 2012.

Munich and the Upper Bavaria area are among the regions in Europe with
the highest quality of living. The area is also home to a large number
of high-tech, services and manufacturing companies, including many
that use NLP technology.

If you are interested in applying please send me:
* a cover letter including possible start dates
* a CV
* a statement of research interests
* (post-doc position): a brief statement of teaching experience and interest
* the names and addresses of two references

Informal inquiries about these or future positions are also welcome, please
include a copy of your CV.

LMU is an equal opportunity employer and is committed to increasing
the diversity of its staff. We strongly encourage applications from
qualified women. Applicants with disabilities will be given
preference in case of equally qualified candidates.

Thanks,
Alexander Fraser

email: fraser@cis.uni-muenchen.de
web: http://www.cis.uni-muenchen.de/~fraser
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Message: 2
Date: Thu, 6 Oct 2016 11:20:01 -0400
From: Michael Denkowski <michael.j.denkowski@gmail.com>
Subject: Re: [Moses-support] Age feature
To: Marwa Refaie <basmallah@hotmail.com>
Cc: Moses <moses-support@mit.edu>
Message-ID:
<CA+-Geg+mgMZ6r+Tm9-NYoPp+BPYGnOp05McUKHyEDwf+wBBWVQ@mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

Hi Marwa,

The cache-based model, the bitext sampling model, and the cdec
implementation are three different things. The cache-based model is
described in this paper
<https://www.matecat.com/wp-content/uploads/2013/09/mt-summit-2013-bertoldi-et-al.pdf>
and
there are directions for using it in Moses here
<http://www.statmt.org/moses/?n=Advanced.CacheBased#ntoc1>.
PhraseDictionaryBitextSampling is described in this paper
<https://ufal.mff.cuni.cz/pbml/104/art-germann.pdf> and data can be added
at runtime with an XML tag in the form:
<update name="PhraseDictionaryName" source="source sentence"
target="target sentence" alignment="0-0 1-1" />

To understand the cdec post-edit support feature and how to get a similar
(better) version in Moses, consider having two phrase tables: a
"background" table that contains initial training data and is static, and a
"post-edit" table that starts empty and has post-edited translations
incrementally added. In Moses, this can be done with two instances of
PhraseDictionaryBitextSampling (one built on all initial data and one built
on empty files) and a PhraseDictionaryGroup
<http://www.statmt.org/moses/?n=Advanced.Models#ntoc7>. After each
sentence is post-edited, the new source-reference pair is aligned and added
to the post-edit table. Each table has a full feature set so each phrase
pair is scored according to both the background and the post-edited data.

cdec just uses a single table that is built on initial data and also
updated with post-edited data. The post-edit support feature flags which
phrases would still be extracted when considering only the post-edited
data. Using two phrase tables with full features (the Moses way) has been
shown to perform better.

Best,
Michael


On Wed, Oct 5, 2016 at 9:45 AM, Marwa Refaie <basmallah@hotmail.com> wrote:

> Hi All
>
>
> Does the "age" feature in the CBMT implemented in the Bitextsampling
> (using suffix array) which is equal to the 'Post-edit support"
> introduced by Denkoweki in cdec ??
>
>
> Any info please !
>
>
>
> *Marwa N. Refaie*
>
>
> _______________________________________________
> Moses-support mailing list
> Moses-support@mit.edu
> http://mailman.mit.edu/mailman/listinfo/moses-support
>
>
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