Moses-support Digest, Vol 128, Issue 7

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Today's Topics:

1. Re: NPLM compilation error (Hieu Hoang)
2. FINAL REMINDER FOR SUBMISSIONS TO DiscoMT 2017 at EMNLP 2017
(Jorg Tiedemann)
3. Error while testing data (Pritesh Ranjan)


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Message: 1
Date: Mon, 5 Jun 2017 12:58:15 -0400
From: Hieu Hoang <hieuhoang@gmail.com>
Subject: Re: [Moses-support] NPLM compilation error
To: Sanjanashree Palanivel <sanjanashree@gmail.com>
Cc: moses-support <moses-support@mit.edu>
Message-ID:
<CAEKMkbhrdTm=Ph97NFypsYr=oNitVz8ehrwuhPo5Y3hJ6itqEg@mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

what is the EXACT command you ran to get this error? Are you sure the boost
library is installed on your computer?


* Looking for MT/NLP opportunities *
Hieu Hoang
http://moses-smt.org/


On 5 June 2017 at 05:20, Sanjanashree Palanivel <sanjanashree@gmail.com>
wrote:

> Dear all,
>
> I am using boost ver 1.50 and eigen 3.3 when I try it make install in nplm
> I am gettin the folllowing error. How to rectify this. It would be grt if I
> can get an earnest reply
>
> g++ -fopenmp -L/path/to/boost_1_58_0/lib -Wl,-rpath
> -Wl,/home/Documents/SMT/boost_1_58_0/lib trainNeuralNetwork.o util.o
> model.o -lboost_iostreams -lboost_system -lboost_filesystem -o
> trainNeuralNetwork
> /usr/bin/ld: cannot find -lboost_iostreams
> /usr/bin/ld: cannot find -lboost_system
> /usr/bin/ld: cannot find -lboost_filesystem
> collect2: error: ld returned 1 exit status
> Makefile:166: recipe for target 'trainNeuralNetwork' failed
> make: *** [trainNeuralNetwork] Error 1
>
>
> --
> Thanks and regards,
>
> Sanjanasri J.P
>
> _______________________________________________
> Moses-support mailing list
> Moses-support@mit.edu
> http://mailman.mit.edu/mailman/listinfo/moses-support
>
>
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Message: 2
Date: Mon, 5 Jun 2017 21:05:00 +0300
From: Jorg Tiedemann <tiedeman@gmail.com>
Subject: [Moses-support] FINAL REMINDER FOR SUBMISSIONS TO DiscoMT
2017 at EMNLP 2017
To: moses-support <moses-support@mit.edu>
Message-ID: <5A5726BA-D195-4AE5-93F8-DBFA3B311793@gmail.com>
Content-Type: text/plain; charset="utf-8"

THIRD WORKSHOP ON DISCOURSE IN MACHINE TRANSLATION (DiscoMT'17)
http://www.idiap.ch/workshop/DiscoMT <http://www.idiap.ch/workshop/DiscoMT>

Paper submission deadline: Friday, June 9, 2017
Decision notification: Wednesday, July 5, 2017
Final versions due: Friday, July 14, 2017


DOUBLE SUBMISSION POLICY

We like to point out that we accept double submissions, for example, papers that are currently under review for EMNLP 2017. But authors should mention that their paper is also submitted elsewhere when sending it to DiscoMT (use the summary field) and we also ask to inform us as soon as possible if the paper is withdrawn from DiscoMT. Thank you!


SUBMISSION TYPES

Long papers (8 pages + references)
Short papers (4 pages + references)


TOPICS

- MT beyond the translation of isolated sentences
- MT techniques for obtaining document-level consistency and domain adaptability
- MT techniques for structured documents
- uses of MT in processing discourse-level phenomena
- techniques for evaluating the effect discourse-aware MT
- quantitative studies on the impact of discourse-level phenomena on MT


CO-CHAIRS AND ORGANIZERS

Bonnie Webber, University of Edinburgh
Andrei Popescu-Belis, Idiap Research Institute
J?rg Tiedemann, University of Helsinki


PROGRAM COMMITTEE

Mauro Cettolo, Fondazione Bruno Kessler, Trento, Italy
Filip Ginter, University of Turku, Finland
Liane Guillou, Brainnwave, Edinburgh, UK
Christian Hardmeier, University of Uppsala, Sweden
Shafiq Joty, Qatar Computing Research Institute, Doha, Qatar
Lori Levin, Carnegie Mellon University, Pittsburgh, PA, USA
Sharid Loaiciga Sanchez, University of Geneva, Switzerland
Ekaterina Lapshinova-Koltunski, Saarland University, DE
Ngoc-Quang Luong, Nuance Communications, Belgium
Thomas Meyer, Google, Zurich, Switzerland
Preslav Nakov, Qatar Computing Research Institute, Doha, Qatar
Michal Novak, Charles University, Prague CZ
Maja Popovic, DFKI, Berlin DE
Annette Rios, University of Zurich, Switzerland
Rico Sennrich, University of Edinburgh, UK
Lucia Specia, University of Sheffield, UK
Sara Stymne, University of Uppsala, Uppsala, Sweden
Yannick Versley, University of Heidelberg, Heidelberg, Germany
Martin Volk, University of Zurich, Switzerland
Min Zhang, Soochow University, Suzhou, China
Sandrine Zufferey, University of Bern, Switzerland

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Message: 3
Date: Tue, 6 Jun 2017 18:03:08 +0530
From: Pritesh Ranjan <pr1817@it.jgec.ac.in>
Subject: [Moses-support] Error while testing data
To: moses-support@mit.edu
Message-ID:
<CAGmx-8g0bwO7j2j6NGoX7Yx4O+HSm9KCtmiKG9BLKeZvomvbEg@mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

Dear Sir/Ma'am,

I am trying to run moses decoder for english to hindi translation but moses is
unable to load the phrase table as it runs out of memory and process
exits.Also when I tried to use the binarized form of phrase table I was
getting the following error.

>~/smt/mosesdecoder/bin/moses -f ~/set_eng_to_hindi/tune/mert-work/moses.ini
<test.true.en >test.out.hi.text

"Loading PhrasePenalty0Loading LexicalReordering0
Loading table into memory...Exception: vector::_M_range_check
"
I have also attached the moses.ini file.

It would be very helpful if someone can help.

# MERT optimized configuration
# decoder /home/iit/mosesdecoder/bin/moses
# BLEU 0.00292786 on dev /home/iit/corpora/parallel/dev.en.clean.txt.txt
# We were before running iteration 2
# finished Tue Jun 6 12:55:43 IST 2017
### MOSES CONFIG FILE ###
#########################

# input factors
[input-factors]
0

# mapping steps
[mapping]
0 T 0

[distortion-limit]
6

# feature functions
[feature]
UnknownWordPenalty
WordPenalty
PhrasePenalty
PhraseDictionaryCompact name=TranslationModel0 num-features=4
path=/home /iit/corpora/parallel/binarised-model/phrase-table.binphr
input-factor=0 output-factor=0
LexicalReordering name=LexicalReordering0 num-features=6 type=wbe-
msd-bidirectional-fe-allff input-factor=0
output-factor=0 path=/home

/iit/corpora/parallel/binarised-model/reordering-table.binlexr
Distortion
IRSTLM name=LM0 factor=0 path=/home/iit/corpora/parallel
/lm/corpora_train.lm.arpa.binary order=3

# dense weights for feature functions
[weight]

LexicalReordering0= 0.0652174 0.0652174 0.0652174 0.0652174 0.0652174
0.0652174
Distortion0= 0.0652174
LM0= 0.108696
WordPenalty0= -0.217391
PhrasePenalty0= 0.0434783
TranslationModel0= 0.0434783 0.0434783 0.0434783 0.0434783
UnknownWordPenalty0= 1


Thanks and Regards

_Pritesh Ranjan
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