Hi Everybody,
I am also facing problem with Mosesdecoder.v211 moseserver. I have
compiled the moseserver with out any error and the moseserver is
listenening to the port also. But when a translation request is going from
the interface, there is no responds from the mosesserver.
I am getting the folowing exception.. org.apache.xmlrpc.XmlRpcException:
Failed to read server's response: Connection refused
Is there any difference in the moseswerver connection of Mosesdecoder
Release 1.0 and Mosesdecoder.V211. ?
could anybody please clarify these doubts and how can I establish the
moseserver connection..
Regards
Lakshya
Message: 1
Date: Mon, 7 Apr 2014 18:25:35 +0100
From: kamel nebhi <k.ne...@sheffield.ac.uk>
Subject: [Moses-support] moses server segmentation fault (core dumped)
To: moses-support <moses-support@mit.edu>
Message-ID:
<CAG66Y3c2UFq5+
2w4eV00RrwVrMWTtVxpQ7=jgxfwng+afnr...@mail.gmail.com>
Content-Type: text/plain; charset="utf-8"
Hi,
I try to install mosesserver on localhost. I have installed xml-rpc and
rebuild moses with no problem.
Next i use this command to run the server : *~/mosesdecoder/bin/mosesserver
-f working/model/moses.ini --server-port 8999*
But it failed with this message :
Defined parameters (per moses.ini or switch):
config: /home/kamelnebhi/recaser/training/moses.ini
distortion-limit: 6
feature: UnknownWordPenalty WordPenalty PhrasePenalty
PhraseDictionaryMemory name=TranslationModel0 table-limit=20 num-features=4
path=/home/kamelnebhi/recaser/training/phrase-table.gz input-factor=0
output-factor=0 Distortion KENLM lazyken=0 name=LM0 factor=0
path=/home/kamelnebhi/recaser/training//cased.srilm.gz order=3
input-factors: 0
mapping: 0 T 0
weight: UnknownWordPenalty0= 1 WordPenalty0= -1 PhrasePenalty0= 0.2
TranslationModel0= 0.2 0.2 0.2 0.2 Distortion0= 0.3 LM0= 0.5
/home/kamelnebhi/mosesdecoder/bin
line=UnknownWordPenalty
FeatureFunction: UnknownWordPenalty0 start: 0 end: 0
line=WordPenalty
FeatureFunction: WordPenalty0 start: 1 end: 1
line=PhrasePenalty
FeatureFunction: PhrasePenalty0 start: 2 end: 2
line=PhraseDictionaryMemory name=TranslationModel0 table-limit=20
num-features=4 path=/home/kamelnebhi/recaser/training/phrase-table.gz
input-factor=0 output-factor=0
FeatureFunction: TranslationModel0 start: 3 end: 6
line=Distortion
FeatureFunction: Distortion0 start: 7 end: 7
line=KENLM lazyken=0 name=LM0 factor=0
path=/home/kamelnebhi/recaser/training//cased.srilm.gz order=3
FeatureFunction: LM0 start: 8 end: 8
Loading the LM will be faster if you build a binary file.
Reading /home/kamelnebhi/recaser/training//cased.srilm.gz
----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100
*The ARPA file is missing <unk>. Substituting log10 probability -100.
***************************************************************************************************
Loading UnknownWordPenalty0
Loading WordPenalty0
Loading PhrasePenalty0
Loading Distortion0
Loading LM0
Loading TranslationModel0
Start loading text SCFG phrase table. Moses format : [3.69361] seconds
Reading /home/kamelnebhi/recaser/training/phrase-table.gz
----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100
****************************************************************************************************
Erreur de segmentation (core dumped)
root@kamelnebhi-MacBookPro:/home/kamelnebhi#
/home/kamelnebhi/mosesdecoder/bin/mosesserver -f
/home/kamelnebhi/recaser/training/moses.ini --server-port 80
Defined parameters (per moses.ini or switch):
config: /home/kamelnebhi/recaser/training/moses.ini
distortion-limit: 6
feature: UnknownWordPenalty WordPenalty PhrasePenalty
PhraseDictionaryMemory name=TranslationModel0 table-limit=20 num-features=4
path=/home/kamelnebhi/recaser/training/phrase-table.gz input-factor=0
output-factor=0 Distortion KENLM lazyken=0 name=LM0 factor=0
path=/home/kamelnebhi/recaser/training//cased.srilm.gz order=3
input-factors: 0
mapping: 0 T 0
weight: UnknownWordPenalty0= 1 WordPenalty0= -1 PhrasePenalty0= 0.2
TranslationModel0= 0.2 0.2 0.2 0.2 Distortion0= 0.3 LM0= 0.5
/home/kamelnebhi/mosesdecoder/bin
line=UnknownWordPenalty
FeatureFunction: UnknownWordPenalty0 start: 0 end: 0
line=WordPenalty
FeatureFunction: WordPenalty0 start: 1 end: 1
line=PhrasePenalty
FeatureFunction: PhrasePenalty0 start: 2 end: 2
line=PhraseDictionaryMemory name=TranslationModel0 table-limit=20
num-features=4 path=/home/kamelnebhi/recaser/training/phrase-table.gz
input-factor=0 output-factor=0
FeatureFunction: TranslationModel0 start: 3 end: 6
line=Distortion
FeatureFunction: Distortion0 start: 7 end: 7
line=KENLM lazyken=0 name=LM0 factor=0
path=/home/kamelnebhi/recaser/training//cased.srilm.gz order=3
FeatureFunction: LM0 start: 8 end: 8
Loading the LM will be faster if you build a binary file.
Reading /home/kamelnebhi/recaser/training//cased.srilm.gz
----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100
*The ARPA file is missing <unk>. Substituting log10 probability -100.
***************************************************************************************************
Loading UnknownWordPenalty0
Loading WordPenalty0
Loading PhrasePenalty0
Loading Distortion0
Loading LM0
Loading TranslationModel0
Start loading text SCFG phrase table. Moses format : [3.69152] seconds
Reading /home/kamelnebhi/recaser/training/phrase-table.gz
----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100
****************************************************************************************************
Segmentation fault (core dumped)
*Thanks for your help*
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------------------------------
---------- Forwarded message ----------
From: <moses-support-requ...@mit.edu>
Date: Tue, Apr 8, 2014 at 3:57 AM
Subject: Moses-support Digest, Vol 90, Issue 19
To: moses-support@mit.edu
Send Moses-support mailing list submissions to
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To subscribe or unsubscribe via the World Wide Web, visit
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When replying, please edit your Subject line so it is more specific
than "Re: Contents of Moses-support digest..."
Today's Topics:
1. moses server segmentation fault (core dumped) (kamel nebhi)
2. Re: Monolingual Word alignment (Philipp Koehn)
3. Call for Participation: Automatic and Manual Metrics for
Operational Translation Evaluation (Lucia Specia)
----------------------------------------------------------------------
Message: 1
Date: Mon, 7 Apr 2014 18:25:35 +0100
From: kamel nebhi <k.ne...@sheffield.ac.uk>
Subject: [Moses-support] moses server segmentation fault (core dumped)
To: moses-support <moses-support@mit.edu>
Message-ID:
<CAG66Y3c2UFq5+2w4eV00RrwVrMWTtVxpQ7=jgxfwng+afnr...@mail.gmail.com>
Content-Type: text/plain; charset="utf-8"
Hi,
I try to install mosesserver on localhost. I have installed xml-rpc and
rebuild moses with no problem.
Next i use this command to run the server : *~/mosesdecoder/bin/mosesserver
-f working/model/moses.ini --server-port 8999*
But it failed with this message :
Defined parameters (per moses.ini or switch):
config: /home/kamelnebhi/recaser/training/moses.ini
distortion-limit: 6
feature: UnknownWordPenalty WordPenalty PhrasePenalty
PhraseDictionaryMemory name=TranslationModel0 table-limit=20 num-features=4
path=/home/kamelnebhi/recaser/training/phrase-table.gz input-factor=0
output-factor=0 Distortion KENLM lazyken=0 name=LM0 factor=0
path=/home/kamelnebhi/recaser/training//cased.srilm.gz order=3
input-factors: 0
mapping: 0 T 0
weight: UnknownWordPenalty0= 1 WordPenalty0= -1 PhrasePenalty0= 0.2
TranslationModel0= 0.2 0.2 0.2 0.2 Distortion0= 0.3 LM0= 0.5
/home/kamelnebhi/mosesdecoder/bin
line=UnknownWordPenalty
FeatureFunction: UnknownWordPenalty0 start: 0 end: 0
line=WordPenalty
FeatureFunction: WordPenalty0 start: 1 end: 1
line=PhrasePenalty
FeatureFunction: PhrasePenalty0 start: 2 end: 2
line=PhraseDictionaryMemory name=TranslationModel0 table-limit=20
num-features=4 path=/home/kamelnebhi/recaser/training/phrase-table.gz
input-factor=0 output-factor=0
FeatureFunction: TranslationModel0 start: 3 end: 6
line=Distortion
FeatureFunction: Distortion0 start: 7 end: 7
line=KENLM lazyken=0 name=LM0 factor=0
path=/home/kamelnebhi/recaser/training//cased.srilm.gz order=3
FeatureFunction: LM0 start: 8 end: 8
Loading the LM will be faster if you build a binary file.
Reading /home/kamelnebhi/recaser/training//cased.srilm.gz
----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100
*The ARPA file is missing <unk>. Substituting log10 probability -100.
***************************************************************************************************
Loading UnknownWordPenalty0
Loading WordPenalty0
Loading PhrasePenalty0
Loading Distortion0
Loading LM0
Loading TranslationModel0
Start loading text SCFG phrase table. Moses format : [3.69361] seconds
Reading /home/kamelnebhi/recaser/training/phrase-table.gz
----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100
****************************************************************************************************
Erreur de segmentation (core dumped)
root@kamelnebhi-MacBookPro:/home/kamelnebhi#
/home/kamelnebhi/mosesdecoder/bin/mosesserver -f
/home/kamelnebhi/recaser/training/moses.ini --server-port 80
Defined parameters (per moses.ini or switch):
config: /home/kamelnebhi/recaser/training/moses.ini
distortion-limit: 6
feature: UnknownWordPenalty WordPenalty PhrasePenalty
PhraseDictionaryMemory name=TranslationModel0 table-limit=20 num-features=4
path=/home/kamelnebhi/recaser/training/phrase-table.gz input-factor=0
output-factor=0 Distortion KENLM lazyken=0 name=LM0 factor=0
path=/home/kamelnebhi/recaser/training//cased.srilm.gz order=3
input-factors: 0
mapping: 0 T 0
weight: UnknownWordPenalty0= 1 WordPenalty0= -1 PhrasePenalty0= 0.2
TranslationModel0= 0.2 0.2 0.2 0.2 Distortion0= 0.3 LM0= 0.5
/home/kamelnebhi/mosesdecoder/bin
line=UnknownWordPenalty
FeatureFunction: UnknownWordPenalty0 start: 0 end: 0
line=WordPenalty
FeatureFunction: WordPenalty0 start: 1 end: 1
line=PhrasePenalty
FeatureFunction: PhrasePenalty0 start: 2 end: 2
line=PhraseDictionaryMemory name=TranslationModel0 table-limit=20
num-features=4 path=/home/kamelnebhi/recaser/training/phrase-table.gz
input-factor=0 output-factor=0
FeatureFunction: TranslationModel0 start: 3 end: 6
line=Distortion
FeatureFunction: Distortion0 start: 7 end: 7
line=KENLM lazyken=0 name=LM0 factor=0
path=/home/kamelnebhi/recaser/training//cased.srilm.gz order=3
FeatureFunction: LM0 start: 8 end: 8
Loading the LM will be faster if you build a binary file.
Reading /home/kamelnebhi/recaser/training//cased.srilm.gz
----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100
*The ARPA file is missing <unk>. Substituting log10 probability -100.
***************************************************************************************************
Loading UnknownWordPenalty0
Loading WordPenalty0
Loading PhrasePenalty0
Loading Distortion0
Loading LM0
Loading TranslationModel0
Start loading text SCFG phrase table. Moses format : [3.69152] seconds
Reading /home/kamelnebhi/recaser/training/phrase-table.gz
----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100
****************************************************************************************************
Segmentation fault (core dumped)
*Thanks for your help*
-------------- next part --------------
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------------------------------
Message: 2
Date: Mon, 7 Apr 2014 16:00:47 -0400
From: Philipp Koehn <pko...@inf.ed.ac.uk>
Subject: Re: [Moses-support] Monolingual Word alignment
To: Mostafa Dehghani <dehghani.most...@gmail.com>
Cc: "moses-support@mit.edu" <moses-support@mit.edu>
Message-ID:
<CAAFADDBXoZv7=u5rqjay3brfwob92ywkrvfekdz6yrx29m7...@mail.gmail.com>
Content-Type: text/plain; charset=ISO-8859-1
Hi,
this outcome is not that surprising to me.
If you align identical sentences, then translating each word to itself
is a pretty good model.
Since your goal is paraphrasing words into synonyms, you should
rather use methods such as the one proposed by Bannard and
Callison-Burch: http://acl.ldc.upenn.edu/P/P05/P05-1074.pdf
-phi
On Sun, Apr 6, 2014 at 11:11 AM, Mostafa Dehghani
<dehghani.most...@gmail.com> wrote:
> Dear all,
>
> I am working on a method for Multilingual Information Retrieval. In my
> method I expand the text of each document by probabilistically translating
> its words to other languages' words (interlingual expansion). However, to
> pass some axioms, I need to expand text of each document in its own
language
> (intralingual expansion). So, beside bilingual word alignments, I need
> monolingual word alignments table (that probably contains the alignment of
> each word to the words those are related/concurred with that word). To do
> so, I used one side of each language sentences and their copy as parallel
> corpus. Then I used the following command:
>
>
> train-model.perl -root-dir train -corpus corpus/fr-fr -f fr1 -e fr2
> -alignment grow-diag-final-and -reordering msd-bidirectional-fe
> -external-bin-dir externalbin -last-step 4
>
>
> such that fr-fr.fr1 fr-fr.fr2 are the same files containing French
> sentences.
> However, I got f2e and e2f files that are only contain alignments of each
> word to itself with probability of 1.
> I am wondering is there any parameter that I should set to achieve words
> alignments (e2f/f2e) those are proper for intralingual expansion?
>
> Regards,
>
> --
> Mostafa
> ,
>
> http://khorshid.ut.ac.ir/~m.dehghani
>
> _______________________________________________
> Moses-support mailing list
> Moses-support@mit.edu
> http://mailman.mit.edu/mailman/listinfo/moses-support
>
------------------------------
Message: 3
Date: Mon, 7 Apr 2014 23:27:07 +0100
From: Lucia Specia <lspe...@gmail.com>
Subject: [Moses-support] Call for Participation: Automatic and Manual
Metrics for Operational Translation Evaluation
To: moses-support@mit.edu, wmt-ta...@googlegroups.com
Message-ID:
<caaleuxzvnsv0xp-uvxok0z8qwtkd-jt11ftoporho0ras9u...@mail.gmail.com>
Content-Type: text/plain; charset="iso-8859-1"
Dear all,
This workshop may be relevant for those of you interested in MT evaluation
metrics.
----
Automatic and Manual Metrics for Operational Translation Evaluation
http://mte2014.github.io/
26 May 2014
Workshop at Language Resources and Evaluation Conference (LREC) 2014
http://lrec2014.lrec-conf.org
In brief:
We invite you to join us for an interesting day of work (and play!) as we
discuss metrics for machine translation quality assessment and participate
in some hands-on task-based translation evaluation.
This workshop on Automatic and Manual Metrics for Operational Translation
Evaluation (MTE 2014) will be a full-day LREC workshop to be held on
Monday, May 26, 2014 in Reykjavik, Iceland. The format of MTE 2014 will be
interactive and energizing: a half-day of short presentations and
discussion of recent work on machine translation quality assessment,
followed by a half-day of hands-on collaborative work with MT metrics that
show promise for the prediction of task suitability of MT output. The
afternoon hands-on work will follow from the morning's presentations, with
some of the hands-on exercises developed directly from the submissions to
the workshop.
Details:
While a significant body of work has been done by the machine translation
(MT) research community towards the development and meta-evaluation of
automatic metrics to assess overall MT quality, less attention has been
dedicated to more operational evaluation metrics aimed at testing whether
translations are adequate within a specific context: purpose, end-user,
task, etc., and why the MT system fails in some cases. Both of these can
benefit from some form of manual analysis. Most work in this area is
limited to productivity tests (e.g. contrasting time for human translation
and MT post-editing). A few initiatives consider more detailed metrics for
the problem, which can also be used to understand and diagnose errors in MT
systems. These include the Multidimensional Quality Metrics (MQM) recently
proposed by the EU F7 project QTLaunchPad, the TAUS Dynamic Quality
Framework, and past projects such as FEMTI, EAGLES and ISLE. Some of these
metrics are also applicable to human translation evaluation. A number of
task-based metrics have also been proposed for applications such as topic
ID / triage and reading comprehension. The purpose of this workshop is to
bring together representatives from academia, industry and government
institutions to discuss and assess metrics for manual and automatic quality
evaluation, with an eye toward how they might be leveraged or further
developed into task-based metrics for more objective "fitness for purpose"
assessment. We will also consider comparisons to well-established metrics
for automatic evaluation such as BLEU, METEOR and others, including
reference-less metrics for quality prediction. The workshop will benefit
from datasets already collected and manually annotated for translation
errors by the QTLaunchPad project (http://www.qt21.eu/launchpad/) and will
cover concepts from many the metrics proposed by participants in the
half-day of hands-on tasks.
Up-to-the-minute information and (most importantly) Registration:
Additional details and schedule will be posted at the workshop website
http://mte2014.github.io/ as they become available. Register to attend via
the LREC registration site at http://lrec2014.lrec-conf.org/en/registration/
.
We look forward to seeing you there!
The MTE 2014 Organizing Committee
Keith J. Miller (MITRE)
Lucia Specia (University of Sheffield)
Kim Harris (GALA and text & form)
Stacey Bailey (MITRE)
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