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Today's Topics:
1. Machine translation (Shraddha J H)
2. Re: EMS results - makes sense ? (Vincent Nguyen)
----------------------------------------------------------------------
Message: 1
Date: Tue, 4 Aug 2015 13:56:36 +0530
From: Shraddha J H <shraddhajh19@gmail.com>
Subject: [Moses-support] Machine translation
To: moses-support@mit.edu
Message-ID:
<CAG+ZA78=WBSkKmx-nHWh1NKk_B=mu01WmPN9shHTGT2nXniqxA@mail.gmail.com>
Content-Type: text/plain; charset="utf-8"
Hi,
I am working on machine translation for embedded systems.
Looking for integration and development of machine translation on embedded
platforms using java script, would like to know the options, feasibility
in-terms of memory.
Regards,
Jina
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Message: 2
Date: Wed, 5 Aug 2015 09:31:34 +0200
From: Vincent Nguyen <vnguyen@neuf.fr>
Subject: Re: [Moses-support] EMS results - makes sense ?
To: Barry Haddow <bhaddow@inf.ed.ac.uk>, Hieu Hoang
<hieuhoang@gmail.com>, moses-support <moses-support@mit.edu>
Message-ID: <55C1BBD6.5040705@neuf.fr>
Content-Type: text/plain; charset="windows-1252"
I increased the disk space but the hierarchical training crashes for
another reason I do not understand.
it's at build_ttable time
I attached the error + config
cheers,
Vincent
Le 04/08/2015 17:28, Barry Haddow a ?crit :
> Hi Vincent
>
> If you are comparing to the results of WMT11, then you can look at the
> system descriptions to see what the authors did. In fact it's worth
> looking at the WMT14 descriptions (WMT15 will be available next month)
> to see how state-of-the-art systems are built.
>
> For fr-en or en-fr, the first thing to look at is the data. There are
> some large data sets released for WMT and you can get a good gain from
> just crunching more data (monolingual and parallel). Unfortunately
> this takes more resources (disk, cpu etc) so you may run into trouble
> here.
>
> The hierarchical models are much bigger so yes you will need more
> disk. For fr-en/en-fr it's probably not worth the extra effort,
>
> cheers - Barry
>
> On 04/08/15 15:58, Vincent Nguyen wrote:
>> thanks for your insights.
>>
>> I am just stuck by the Bleu difference between my 26 and the 30 of
>> WMT11, and some results of WMT14 close to 36 or even 39
>>
>> I am currently having trouble with hierarchical rule set instead of
>> lexical reordering
>> wondering if I will get better results but I have an error message
>> filesystem root low disk space before it crashes.
>> is this model taking more disk space in some ways ?
>>
>> I will next try to use more corpora of which in domain with my
>> internal TMX
>>
>> thanks for your answers.
>>
>> Le 04/08/2015 16:02, Hieu Hoang a ?crit :
>>>
>>> On 03/08/2015 13:00, Vincent Nguyen wrote:
>>>> Hi,
>>>>
>>>> Just a heads up on some EMS results, to get your experienced opinions.
>>>>
>>>> Corpus: Europarlv7 + NC2010
>>>> fr => en
>>>> Evaluation NC2011.
>>>>
>>>> 1) IRSTLM vs KenLM is much slower for training / tuning.
>>> that sounds right. KenLM is also multithreaded, IRSTLM can only be
>>> used in single-threaded decoding.
>>>> 2) BLEU results are almost the same (25.7 with Irstlm, 26.14 with
>>>> KenLM)
>>> true
>>>> 3) Compact Mode is faster than onDisk with a short test (77
>>>> segments 96
>>>> seconds, vs 126 seconds)
>>> true
>>>> 4) One last thing I do not understand though :
>>>> For sake of checking, I replaced NC2011 by NC2010 in the evaluation (I
>>>> know since NC2010 is part of training, should not be relevant)
>>>> I got roughly the same BLEU score. I would have expected a higher
>>>> score
>>>> with a test set inculded in the training corpus.
>>>>
>>>> makes sense ?
>>>>
>>>>
>>>> Next steps :
>>>> What path should I use to get better scores ? I read the 'optimize'
>>>> section of the website which deals more with speed
>>>> and of course I will appply all of this but I was interested in
>>>> tips to
>>>> get more quality if possible.
>>> look into domain adaptation if you have multiple training corpora,
>>> some of which is in-domain and some out-of-domain.
>>>
>>> Other than that, getting good bleu score is a research open question.
>>>
>>> Well done on getting this far
>>>>
>>>> Thanks
>>>>
>>>>
>>>>
>>>> _______________________________________________
>>>> Moses-support mailing list
>>>> Moses-support@mit.edu
>>>> http://mailman.mit.edu/mailman/listinfo/moses-support
>>>>
>> _______________________________________________
>> Moses-support mailing list
>> Moses-support@mit.edu
>> http://mailman.mit.edu/mailman/listinfo/moses-support
>>
>
>
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################################################
### CONFIGURATION FILE FOR AN SMT EXPERIMENT ###
################################################
[GENERAL]
### directory in which experiment is run
#
working-dir = /home/moses/working
# specification of the language pair
input-extension = fr
output-extension = en
pair-extension = fr-en
### directories that contain tools and data
#
# moses
moses-src-dir = /home/moses/mosesdecoder
#
# moses binaries
moses-bin-dir = $moses-src-dir/bin
#
# moses scripts
moses-script-dir = $moses-src-dir/scripts
#
# directory where GIZA++/MGIZA programs resides
external-bin-dir = /home/moses/working/bin/training-tools/mgizapp
#
# srilm
srilm-dir = $moses-src-dir/srilm/bin/i686-m64
#
# irstlm
irstlm-dir = $moses-src-dir/irstlm/bin
#
# randlm
#randlm-dir = $moses-src-dir/randlm/bin
#
# data
wmt12-data = $working-dir/data
### basic tools
#
# moses decoder
decoder = $moses-bin-dir/moses
# conversion of rule table into binary on-disk format
#ttable-binarizer = "$moses-bin-dir/CreateOnDiskPt 1 1 4 100 2"
ttable-binarizer = "$moses-bin-dir/processPhraseTableMin"
# tokenizers - comment out if all your data is already tokenized
input-tokenizer = "$moses-script-dir/tokenizer/tokenizer.perl -a -l $input-extension"
output-tokenizer = "$moses-script-dir/tokenizer/tokenizer.perl -a -l $output-extension"
# truecasers - comment out if you do not use the truecaser
input-truecaser = $moses-script-dir/recaser/truecase.perl
output-truecaser = $moses-script-dir/recaser/truecase.perl
detruecaser = $moses-script-dir/recaser/detruecase.perl
# lowercaser - comment out if you use truecasing
#input-lowercaser = $moses-script-dir/tokenizer/lowercase.perl
#output-lowercaser = $moses-script-dir/tokenizer/lowercase.perl
### generic parallelizer for cluster and multi-core machines
# you may specify a script that allows the parallel execution
# parallizable steps (see meta file). you also need specify
# the number of jobs (cluster) or cores (multicore)
#
#generic-parallelizer = $moses-script-dir/ems/support/generic-parallelizer.perl
generic-parallelizer = $moses-script-dir/ems/support/generic-multicore-parallelizer.perl
### cluster settings (if run on a cluster machine)
# number of jobs to be submitted in parallel
#
#jobs = 10
# arguments to qsub when scheduling a job
#qsub-settings = ""
# project for priviledges and usage accounting
#qsub-project = iccs_smt
# memory and time
#qsub-memory = 4
#qsub-hours = 48
### multi-core settings
# when the generic parallelizer is used, the number of cores
# specified here
cores = 8
#################################################################
# PARALLEL CORPUS PREPARATION:
# create a tokenized, sentence-aligned corpus, ready for training
[CORPUS]
### long sentences are filtered out, since they slow down GIZA++
# and are a less reliable source of data. set here the maximum
# length of a sentence
#
max-sentence-length = 80
[CORPUS:europarl]
### command to run to get raw corpus files
#
# get-corpus-script =
### raw corpus files (untokenized, but sentence aligned)
#
raw-stem = $wmt12-data/training/europarl-v7.$pair-extension
### tokenized corpus files (may contain long sentences)
#
#tokenized-stem =
### if sentence filtering should be skipped,
# point to the clean training data
#
#clean-stem =
### if corpus preparation should be skipped,
# point to the prepared training data
#
#lowercased-stem =
[CORPUS:nc]
raw-stem = $wmt12-data/training/news-commentary-v7.$pair-extension
[CORPUS:un] IGNORE
raw-stem = $wmt12-data/training/undoc.2000.$pair-extension
#################################################################
# LANGUAGE MODEL TRAINING
[LM]
### tool to be used for language model training
# kenlm training
lm-training = "$moses-script-dir/ems/support/lmplz-wrapper.perl -bin $moses-bin-dir/lmplz"
settings = "--prune '0 0 1' -T $working-dir/lm -S 20%"
# srilm
#lm-training = $srilm-dir/ngram-count
#settings = "-interpolate -kndiscount -unk"
# irstlm training
# msb = modified kneser ney; p=0 no singleton pruning
#lm-training = "$moses-script-dir/generic/trainlm-irst2.perl -cores $cores -irst-dir $irstlm-dir -temp-dir $working-dir/tmp"
#settings = "-s msb -p 0"
# order of the language model
order = 5
### tool to be used for training randomized language model from scratch
# (more commonly, a SRILM is trained)
#
#rlm-training = "$moses-src-dir/randlm/bin/buildlm -falsepos 8 -values 8"
### script to use for binary table format for irstlm or kenlm
# (default: no binarization)
# irstlm
#lm-binarizer = $irstlm-dir/compile-lm
# kenlm, also set type to 8
lm-binarizer = $moses-bin-dir/build_binary
type = 8
### script to create quantized language model format (irstlm)
# (default: no quantization)
#
#lm-quantizer = $irstlm-dir/quantize-lm
### script to use for converting into randomized table format
# (default: no randomization)
#
#lm-randomizer = "$randlm-dir/buildlm -falsepos 8 -values 8"
### each language model to be used has its own section here
[LM:europarl]
### command to run to get raw corpus files
#
#get-corpus-script = ""
### raw corpus (untokenized)
#
raw-corpus = $wmt12-data/training/europarl-v7.$pair-extension.$output-extension
### tokenized corpus files (may contain long sentences)
#
#tokenized-corpus =
### if corpus preparation should be skipped,
# point to the prepared language model
#
#lm =
[LM:nc]
raw-corpus = $wmt12-data/training/news-commentary-v7.$pair-extension.$output-extension
[LM:un] IGNORE
raw-corpus = $wmt12-data/training/undoc.2000.$pair-extension.$output-extension
[LM:news] IGNORE
raw-corpus = $wmt12-data/training/news.$output-extension.shuffled
#################################################################
# INTERPOLATING LANGUAGE MODELS
[INTERPOLATED-LM] IGNORE
# if multiple language models are used, these may be combined
# by optimizing perplexity on a tuning set
# see, for instance [Koehn and Schwenk, IJCNLP 2008]
### script to interpolate language models
# if commented out, no interpolation is performed
#
script = $moses-script-dir/ems/support/interpolate-lm.perl
### tuning set
# you may use the same set that is used for mert tuning (reference set)
#
tuning-sgm = $wmt12-data/dev/newstest2011-ref.$output-extension.sgm
#raw-tuning =
#tokenized-tuning =
#factored-tuning =
#lowercased-tuning =
#split-tuning =
### group language models for hierarchical interpolation
# (flat interpolation is limited to 10 language models)
#group = "first,second fourth,fifth"
### script to use for binary table format for irstlm or kenlm
# (default: no binarization)
# irstlm
#lm-binarizer = $irstlm-dir/compile-lm
# kenlm, also set type to 8
lm-binarizer = $moses-bin-dir/build_binary
type = 8
### script to create quantized language model format (irstlm)
# (default: no quantization)
#
#lm-quantizer = $irstlm-dir/quantize-lm
### script to use for converting into randomized table format
# (default: no randomization)
#
#lm-randomizer = "$randlm-dir/buildlm -falsepos 8 -values 8"
#################################################################
# MODIFIED MOORE LEWIS FILTERING
[MML] IGNORE
### specifications for language models to be trained
#
#lm-training = $srilm-dir/ngram-count
#lm-settings = "-interpolate -kndiscount -unk"
#lm-binarizer = $moses-src-dir/bin/build_binary
#lm-query = $moses-src-dir/bin/query
#order = 5
### in-/out-of-domain source/target corpora to train the 4 language model
#
# in-domain: point either to a parallel corpus
#outdomain-stem = [CORPUS:toy:clean-split-stem]
# ... or to two separate monolingual corpora
#indomain-target = [LM:toy:lowercased-corpus]
#raw-indomain-source = $toy-data/nc-5k.$input-extension
# point to out-of-domain parallel corpus
#outdomain-stem = [CORPUS:giga:clean-split-stem]
# settings: number of lines sampled from the corpora to train each language model on
# (if used at all, should be small as a percentage of corpus)
#settings = "--line-count 100000"
#################################################################
# TRANSLATION MODEL TRAINING
[TRAINING]
### training script to be used: either a legacy script or
# current moses training script (default)
#
script = $moses-script-dir/training/train-model.perl
### general options
# these are options that are passed on to train-model.perl, for instance
# * "-mgiza -mgiza-cpus 8" to use mgiza instead of giza
# * "-sort-buffer-size 8G -sort-compress gzip" to reduce on-disk sorting
# * "-sort-parallel 8 -cores 8" to speed up phrase table building
# * "-parallel" for parallel execution of mkcls and giza
#
training-options = "-mgiza -mgiza-cpus 8"
### factored training: specify here which factors used
# if none specified, single factor training is assumed
# (one translation step, surface to surface)
#
#input-factors = word lemma pos morph
#output-factors = word lemma pos
#alignment-factors = "word -> word"
#translation-factors = "word -> word"
#reordering-factors = "word -> word"
#generation-factors = "word -> pos"
#decoding-steps = "t0, g0"
### parallelization of data preparation step
# the two directions of the data preparation can be run in parallel
# comment out if not needed
#
parallel = yes
### pre-computation for giza++
# giza++ has a more efficient data structure that needs to be
# initialized with snt2cooc. if run in parallel, this may reduces
# memory requirements. set here the number of parts
#
#run-giza-in-parts = 5
### symmetrization method to obtain word alignments from giza output
# (commonly used: grow-diag-final-and)
#
alignment-symmetrization-method = grow-diag-final-and
### use of Chris Dyer's fast align for word alignment
#
#fast-align-settings = "-d -o -v"
### use of berkeley aligner for word alignment
#
#use-berkeley = true
#alignment-symmetrization-method = berkeley
#berkeley-train = $moses-script-dir/ems/support/berkeley-train.sh
#berkeley-process = $moses-script-dir/ems/support/berkeley-process.sh
#berkeley-jar = /your/path/to/berkeleyaligner-1.1/berkeleyaligner.jar
#berkeley-java-options = "-server -mx30000m -ea"
#berkeley-training-options = "-Main.iters 5 5 -EMWordAligner.numThreads 8"
#berkeley-process-options = "-EMWordAligner.numThreads 8"
#berkeley-posterior = 0.5
### use of baseline alignment model (incremental training)
#
#baseline = 68
#baseline-alignment-model = "$working-dir/training/prepared.$baseline/$input-extension.vcb \
# $working-dir/training/prepared.$baseline/$output-extension.vcb \
# $working-dir/training/giza.$baseline/${output-extension}-$input-extension.cooc \
# $working-dir/training/giza-inverse.$baseline/${input-extension}-$output-extension.cooc \
# $working-dir/training/giza.$baseline/${output-extension}-$input-extension.thmm.5 \
# $working-dir/training/giza.$baseline/${output-extension}-$input-extension.hhmm.5 \
# $working-dir/training/giza-inverse.$baseline/${input-extension}-$output-extension.thmm.5 \
# $working-dir/training/giza-inverse.$baseline/${input-extension}-$output-extension.hhmm.5"
### if word alignment should be skipped,
# point to word alignment files
#
#word-alignment = $working-dir/model/aligned.1
### filtering some corpora with modified Moore-Lewis
# specify corpora to be filtered and ratio to be kept, either before or after word alignment
#mml-filter-corpora = toy
#mml-before-wa = "-proportion 0.9"
#mml-after-wa = "-proportion 0.9"
### build memory mapped suffix array phrase table
# (binarizing the reordering table is a good idea, since filtering makes little sense)
#mmsapt = "num-features=9 pfwd=g+ pbwd=g+ smooth=0 sample=1000 workers=1"
binarize-all = $moses-script-dir/training/binarize-model.perl
### create a bilingual concordancer for the model
#
#biconcor = $moses-bin-dir/biconcor
## Operation Sequence Model (OSM)
# Durrani, Schmid and Fraser. (2011):
# "A Joint Sequence Translation Model with Integrated Reordering"
# compile Moses with --max-kenlm-order=9 if higher order is required
#
#operation-sequence-model = "yes"
#operation-sequence-model-order = 5
#operation-sequence-model-settings = "-lmplz '$moses-src-dir/bin/lmplz -S 40% -T $working-dir/model/tmp'"
#
# if OSM training should be skipped, point to OSM Model
#osm-model =
### unsupervised transliteration module
# Durrani, Sajjad, Hoang and Koehn (EACL, 2014).
# "Integrating an Unsupervised Transliteration Model
# into Statistical Machine Translation."
#
#transliteration-module = "yes"
#post-decoding-transliteration = "yes"
### lexicalized reordering: specify orientation type
# (default: only distance-based reordering model)
#
#lexicalized-reordering = msd-bidirectional-fe
### hierarchical rule set
#
hierarchical-rule-set = true
### settings for rule extraction
#
#extract-settings = ""
max-phrase-length = 5
### add extracted phrases from baseline model
#
#baseline-extract = $working-dir/model/extract.$baseline
#
# requires aligned parallel corpus for re-estimating lexical translation probabilities
#baseline-corpus = $working-dir/training/corpus.$baseline
#baseline-alignment = $working-dir/model/aligned.$baseline.$alignment-symmetrization-method
### unknown word labels (target syntax only)
# enables use of unknown word labels during decoding
# label file is generated during rule extraction
#
#use-unknown-word-labels = true
### if phrase extraction should be skipped,
# point to stem for extract files
#
# extracted-phrases =
### settings for rule scoring
#
score-settings = "--GoodTuring --MinScore 2:0.0001"
### include word alignment in phrase table
#
#include-word-alignment-in-rules = yes
### sparse lexical features
#
#sparse-features = "target-word-insertion top 50, source-word-deletion top 50, word-translation top 50 50, phrase-length"
### domain adaptation settings
# options: sparse, any of: indicator, subset, ratio
#domain-features = "subset"
### if phrase table training should be skipped,
# point to phrase translation table
#
# phrase-translation-table =
### if reordering table training should be skipped,
# point to reordering table
#
# reordering-table =
### filtering the phrase table based on significance tests
# Johnson, Martin, Foster and Kuhn. (2007): "Improving Translation Quality by Discarding Most of the Phrasetable"
# options: -n number of translations; -l 'a+e', 'a-e', or a positive real value -log prob threshold
#salm-index = /path/to/project/salm/Bin/Linux/Index/IndexSA.O64
#sigtest-filter = "-l a+e -n 50"
### if training should be skipped,
# point to a configuration file that contains
# pointers to all relevant model files
#
#config =
#####################################################
### TUNING: finding good weights for model components
[TUNING]
### instead of tuning with this setting, old weights may be recycled
# specify here an old configuration file with matching weights
#
#weight-config = $working-dir/tuning/moses.weight-reused.ini.1
### tuning script to be used
#
tuning-script = $moses-script-dir/training/mert-moses.pl
tuning-settings = "-mertdir $moses-bin-dir"
### specify the corpus used for tuning
# it should contain 1000s of sentences
#
input-sgm = $wmt12-data/dev/newstest2011-src.$input-extension.sgm
#raw-input =
#tokenized-input =
#factorized-input =
#input =
#
reference-sgm = $wmt12-data/dev/newstest2011-ref.$output-extension.sgm
#raw-reference =
#tokenized-reference =
#factorized-reference =
#reference =
### size of n-best list used (typically 100)
#
nbest = 100
### ranges for weights for random initialization
# if not specified, the tuning script will use generic ranges
# it is not clear, if this matters
#
# lambda =
### additional flags for the filter script
#
filter-settings = ""
### additional flags for the decoder
#
decoder-settings = "-threads 4"
### if tuning should be skipped, specify this here
# and also point to a configuration file that contains
# pointers to all relevant model files
#
#config-with-reused-weights =
#########################################################
## RECASER: restore case, this part only trains the model
[RECASING]
### training data
# raw input needs to be still tokenized,
# also also tokenized input may be specified
#
#tokenized = [LM:europarl:tokenized-corpus]
### additinal settings
#
recasing-settings = ""
#lm-training = $srilm-dir/ngram-count
decoder = $moses-bin-dir/moses
# already a trained recaser? point to config file
#recase-config =
#######################################################
## TRUECASER: train model to truecase corpora and input
[TRUECASER]
### script to train truecaser models
#
trainer = $moses-script-dir/recaser/train-truecaser.perl
### training data
# data on which truecaser is trained
# if no training data is specified, parallel corpus is used
#
# raw-stem =
# tokenized-stem =
### trained model
#
# truecase-model =
######################################################################
## EVALUATION: translating a test set using the tuned system and score it
[EVALUATION]
### number of jobs (if parallel execution on cluster)
#
#jobs = 10
### additional flags for the filter script
#
#filter-settings = ""
### additional decoder settings
# switches for the Moses decoder
# common choices:
# "-threads N" for multi-threading
# "-mbr" for MBR decoding
# "-drop-unknown" for dropping unknown source words
# "-search-algorithm 1 -cube-pruning-pop-limit 5000 -s 5000" for cube pruning
#
decoder-settings = "-search-algorithm 1 -cube-pruning-pop-limit 5000 -s 5000"
### specify size of n-best list, if produced
#
#nbest = 100
### multiple reference translations
#
#multiref = yes
### prepare system output for scoring
# this may include detokenization and wrapping output in sgm
# (needed for nist-bleu, ter, meteor)
#
detokenizer = "$moses-script-dir/tokenizer/detokenizer.perl -l $output-extension"
#recaser = $moses-script-dir/recaser/recase.perl
wrapping-script = "$moses-script-dir/ems/support/wrap-xml.perl $output-extension"
#output-sgm =
### BLEU
#
nist-bleu = $moses-script-dir/generic/mteval-v13a.pl
nist-bleu-c = "$moses-script-dir/generic/mteval-v13a.pl -c"
#multi-bleu = "$moses-script-dir/generic/multi-bleu.perl -lc"
#multi-bleu-c = $moses-script-dir/generic/multi-bleu.perl
#ibm-bleu =
### TER: translation error rate (BBN metric) based on edit distance
# not yet integrated
#
# ter =
### METEOR: gives credit to stem / worknet synonym matches
## recently integrated - use with care
## only for supported languages, needs to be installed separately
##
## uncomment following 3 lines, modify first one adding the location of meteor installation on your disk
# meteor-script = "/project/software/meteor-1.3/meteor-1.3.jar"
# meteor = "java -Xmx2G -jar $meteor-script"
# meteor-params = " -l $output-extension -norm"
### Analysis: carry out various forms of analysis on the output
#
analysis = $moses-script-dir/ems/support/analysis.perl
#
# also report on input coverage
analyze-coverage = yes
#
# also report on phrase mappings used
report-segmentation = yes
#
# report precision of translations for each input word, broken down by
# count of input word in corpus and model
#report-precision-by-coverage = yes
#
# further precision breakdown by factor
#precision-by-coverage-factor = pos
#
# visualization of the search graph in tree-based models
#analyze-search-graph = yes
[EVALUATION:newstest2011]
### input data
#
input-sgm = $wmt12-data/dev/newstest2011-src.$input-extension.sgm
# raw-input =
# tokenized-input =
# factorized-input =
# input =
### reference data
#
reference-sgm = $wmt12-data/dev/newstest2011-ref.$output-extension.sgm
# raw-reference =
# tokenized-reference =
# reference =
### analysis settings
# may contain any of the general evaluation analysis settings
# specific setting: base coverage statistics on earlier run
#
#precision-by-coverage-base = $working-dir/evaluation/test.analysis.5
### wrapping frame
# for nist-bleu and other scoring scripts, the output needs to be wrapped
# in sgm markup (typically like the input sgm)
#
wrapping-frame = $input-sgm
##########################################
### REPORTING: summarize evaluation scores
[REPORTING]
### currently no parameters for reporting section
-------------- next part --------------
Using SCRIPTS_ROOTDIR: /home/moses/mosesdecoder/scripts
using gzip
(6) score phrases @ Wed Aug 5 01:43:01 CEST 2015
(6.1) creating table half /home/moses/working/model/phrase-table.6.half.f2e @ Wed Aug 5 01:43:01 CEST 2015
/home/moses/mosesdecoder/scripts/generic/score-parallel.perl 8 "sort " /home/moses/mosesdecoder/scripts/../bin/score /home/moses/working/model/extract.6.sorted.gz /home/moses/working/model/lex.5.f2e /home/moses/working/model/phrase-table.6.half.f2e.gz --Hierarchical --GoodTuring 0
Executing: /home/moses/mosesdecoder/scripts/generic/score-parallel.perl 8 "sort " /home/moses/mosesdecoder/scripts/../bin/score /home/moses/working/model/extract.6.sorted.gz /home/moses/working/model/lex.5.f2e /home/moses/working/model/phrase-table.6.half.f2e.gz --Hierarchical --GoodTuring 0
(6.1) creating table half /home/moses/working/model/phrase-table.6.half.e2f @ Wed Aug 5 01:43:01 CEST 2015
/home/moses/mosesdecoder/scripts/generic/score-parallel.perl 8 "sort " /home/moses/mosesdecoder/scripts/../bin/score /home/moses/working/model/extract.6.inv.sorted.gz /home/moses/working/model/lex.5.e2f /home/moses/working/model/phrase-table.6.half.e2f.gz --Inverse --Hierarchical 1
Executing: /home/moses/mosesdecoder/scripts/generic/score-parallel.perl 8 "sort " /home/moses/mosesdecoder/scripts/../bin/score /home/moses/working/model/extract.6.inv.sorted.gz /home/moses/working/model/lex.5.e2f /home/moses/working/model/phrase-table.6.half.e2f.gz --Inverse --Hierarchical 1
using gzip
Started Wed Aug 5 01:43:01 2015
using gzip
Started Wed Aug 5 01:43:01 2015
/home/moses/mosesdecoder/scripts/../bin/score /home/moses/working/model/tmp.24871/extract.0.gz /home/moses/working/model/lex.5.e2f /home/moses/working/model/tmp.24871/phrase-table.half.0000000.gz --Inverse --Hierarchical 2>> /dev/stderr
/home/moses/mosesdecoder/scripts/../bin/score /home/moses/working/model/tmp.24871/extract.1.gz /home/moses/working/model/lex.5.e2f /home/moses/working/model/tmp.24871/phrase-table.half.0000001.gz --Inverse --Hierarchical 2>> /dev/stderr
/home/moses/mosesdecoder/scripts/../bin/score /home/moses/working/model/tmp.24871/extract.2.gz /home/moses/working/model/lex.5.e2f /home/moses/working/model/tmp.24871/phrase-table.half.0000002.gz --Inverse --Hierarchical 2>> /dev/stderr
/home/moses/mosesdecoder/scripts/../bin/score /home/moses/working/model/tmp.24871/extract.3.gz /home/moses/working/model/lex.5.e2f /home/moses/working/model/tmp.24871/phrase-table.half.0000003.gz --Inverse --Hierarchical 2>> /dev/stderr
/home/moses/mosesdecoder/scripts/../bin/score /home/moses/working/model/tmp.24871/extract.4.gz /home/moses/working/model/lex.5.e2f /home/moses/working/model/tmp.24871/phrase-table.half.0000004.gz --Inverse --Hierarchical 2>> /dev/stderr
/home/moses/mosesdecoder/scripts/../bin/score /home/moses/working/model/tmp.24871/extract.5.gz /home/moses/working/model/lex.5.e2f /home/moses/working/model/tmp.24871/phrase-table.half.0000005.gz --Inverse --Hierarchical 2>> /dev/stderr
/home/moses/mosesdecoder/scripts/../bin/score /home/moses/working/model/tmp.24871/extract.6.gz /home/moses/working/model/lex.5.e2f /home/moses/working/model/tmp.24871/phrase-table.half.0000006.gz --Inverse --Hierarchical 2>> /dev/stderr
/home/moses/mosesdecoder/scripts/../bin/score /home/moses/working/model/tmp.24871/extract.7.gz /home/moses/working/model/lex.5.e2f /home/moses/working/model/tmp.24871/phrase-table.half.0000007.gz --Inverse --Hierarchical 2>> /dev/stderr
/home/moses/working/model/tmp.24871/run.0.sh/home/moses/working/model/tmp.24871/run.1.sh/home/moses/working/model/tmp.24871/run.2.sh/home/moses/working/model/tmp.24871/run.3.sh/home/moses/working/model/tmp.24871/run.4.sh/home/moses/working/model/tmp.24871/run.5.sh/home/moses/working/model/tmp.24871/run.6.sh/home/moses/working/model/tmp.24871/run.7.sh/home/moses/mosesdecoder/scripts/../bin/score /home/moses/working/model/tmp.24870/extract.0.gz /home/moses/working/model/lex.5.f2e /home/moses/working/model/tmp.24870/phrase-table.half.0000000.gz --Hierarchical --GoodTuring 2>> /dev/stderr
/home/moses/mosesdecoder/scripts/../bin/score /home/moses/working/model/tmp.24870/extract.1.gz /home/moses/working/model/lex.5.f2e /home/moses/working/model/tmp.24870/phrase-table.half.0000001.gz --Hierarchical --GoodTuring 2>> /dev/stderr
/home/moses/mosesdecoder/scripts/../bin/score /home/moses/working/model/tmp.24870/extract.2.gz /home/moses/working/model/lex.5.f2e /home/moses/working/model/tmp.24870/phrase-table.half.0000002.gz --Hierarchical --GoodTuring 2>> /dev/stderr
/home/moses/mosesdecoder/scripts/../bin/score /home/moses/working/model/tmp.24870/extract.3.gz /home/moses/working/model/lex.5.f2e /home/moses/working/model/tmp.24870/phrase-table.half.0000003.gz --Hierarchical --GoodTuring 2>> /dev/stderr
/home/moses/mosesdecoder/scripts/../bin/score /home/moses/working/model/tmp.24870/extract.4.gz /home/moses/working/model/lex.5.f2e /home/moses/working/model/tmp.24870/phrase-table.half.0000004.gz --Hierarchical --GoodTuring 2>> /dev/stderr
/home/moses/mosesdecoder/scripts/../bin/score /home/moses/working/model/tmp.24870/extract.5.gz /home/moses/working/model/lex.5.f2e /home/moses/working/model/tmp.24870/phrase-table.half.0000005.gz --Hierarchical --GoodTuring 2>> /dev/stderr
/home/moses/mosesdecoder/scripts/../bin/score /home/moses/working/model/tmp.24870/extract.6.gz /home/moses/working/model/lex.5.f2e /home/moses/working/model/tmp.24870/phrase-table.half.0000006.gz --Hierarchical --GoodTuring 2>> /dev/stderr
/home/moses/mosesdecoder/scripts/../bin/score /home/moses/working/model/tmp.24870/extract.7.gz /home/moses/working/model/lex.5.f2e /home/moses/working/model/tmp.24870/phrase-table.half.0000007.gz --Hierarchical --GoodTuring 2>> /dev/stderr
/home/moses/working/model/tmp.24870/run.2.sh/home/moses/working/model/tmp.24870/run.7.sh/home/moses/working/model/tmp.24870/run.6.sh/home/moses/working/model/tmp.24870/run.4.sh/home/moses/working/model/tmp.24870/run.5.sh/home/moses/working/model/tmp.24870/run.0.shgunzip -c /home/moses/working/model/tmp.24870/phrase-table.half.*.gz 2>> /dev/stderr | gzip -c > /home/moses/working/model/phrase-table.6.half.f2e.gz 2>> /dev/stderr gunzip -c /home/moses/working/model/tmp.24871/phrase-table.half.*.gz 2>> /dev/stderr| LC_ALL=C sort -T /home/moses/working/model/tmp.24871 | gzip -c > /home/moses/working/model/phrase-table.6.half.e2f.gz 2>> /dev/stderr rm -rf /home/moses/working/model/tmp.24870
Finished Wed Aug 5 02:53:04 2015
rm -rf /home/moses/working/model/tmp.24871
Finished Wed Aug 5 03:31:26 2015
(6.6) consolidating the two halves @ Wed Aug 5 03:31:26 CEST 2015
Executing: /home/moses/mosesdecoder/scripts/../bin/consolidate /home/moses/working/model/phrase-table.6.half.f2e.gz /home/moses/working/model/phrase-table.6.half.e2f.gz /dev/stdout --Hierarchical --MinScore 2:0.0001 --GoodTuring /home/moses/working/model/phrase-table.6.half.f2e.gz.coc | gzip -c > /home/moses/working/model/phrase-table.6.gz
Consolidate v2.0 written by Philipp Koehn
consolidating direct and indirect rule tables
processing hierarchical rules
setting minScore2 to 0.0001
adjusting phrase translation probabilities with Good Turing discounting
......terminate called after throwing an instance of 'util::Exception'
what(): phrase-extract/consolidate-main.cpp:275 in void processFiles(const string&, const string&, const string&, const string&, const string&, const string&) threw util::Exception because `itemDirect[0].compare( itemIndirect[0] ) != 0'.
target phrase does not match in line 672687: '[X][X] atteigne les endroits [X]' != ') Mesdames et [X][X] [X]'
Aborted (core dumped)
Executing: rm -f /home/moses/working/model/phrase-table.6.half.*
slation probabilities with Good Turing discounting
Loading lexical translation table from /home/moses/working/model/lex.5.f2e.............................................................................................................................................................................................................................................................................
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End of Moses-support Digest, Vol 106, Issue 8
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