Hello Barry
Thank you for your help.
I commneted out the KenLm in the configuration file and used buil-binary to
binarize lm file.( it doesn't create arpa file, should I create it
manually? )
It didn't get any error but it still takes too long to finish the testing
process.
I have 5000 sentences in my test set. I checked test.output.1 file and
noticed that it contains only the translation of 4690 sentences. it seems
that it gets into a loop for translating the 4691'th sentence.
I am afraid that I made a mistake in setting the parameters in my
configuration file. I will be thankful if you could take a look at my file.
Thank you again
amir
On Fri, Feb 14, 2014 at 5:28 AM, Barry Haddow <[email protected]>wrote:
> Hi Amir
>
> Even if you use IRSTLM to build the language model, you can still use
> KenLM for decoding. Make sure you create an arpa file with IRSTLM, then use
> build_binary to binarise it so that it loads quickly with KenLM. Then you
> can use multi-threaded decoding,
>
> cheers - Barry
>
>
> On 14/02/14 13:01, amir haghighi wrote:
>
> Thank you Barry,
>
> I use IRSTLM to build the language model. Can I use multi-thread for
> decoder-setting?
> I get "IRST LM is not threadsafe" error.
>
> I want to use IRSTLM, is there any other way to speed up the tuning step ?
>
> Regrads
>
>
> On Fri, Feb 14, 2014 at 1:53 AM, Barry Haddow
> <[email protected]>wrote:
>
>> Hi Amir
>>
>> You can add
>>
>> decoder-settings = "-threads 4"
>>
>> to your TUNING stanza.
>>
>> Also try
>>
>> filter-settings = "-MinScore 2:0.0001"
>>
>> for more aggressive filtering.
>>
>> Running tuning on a laptop though is always going to be slow,
>>
>> cheers - Barry
>>
>>
>> On 14/02/14 09:26, amir haghighi wrote:
>>
>> Thank you arezki and yohit
>>
>> I don't know how can I change multi-thread setting in ems config file.
>>
>>
>>
>> On Fri, Feb 14, 2014 at 12:36 AM, Arezki Sadoune
>> <[email protected]>wrote:
>>
>>> Hello Amir
>>> I think your tuning process will go faster if you use a multi-threaded
>>> Mert.
>>> /home/mert-moses.pl --threads 4
>>> you have of course tu indicate 8 instead of 4 if your laptop is
>>> equipped with eight cores
>>> Best regards
>>>
>>>
>>> Le Vendredi 14 février 2014 8h27, amir haghighi <
>>> [email protected]> a écrit :
>>> Hello
>>>
>>> I have a corpus with 400'000 sentences for training, 1000 sentences for
>>> tuning and 100'000 sentences for test. I couldn't run ems on my corpus,
>>> after 3 days, with my old laptop.
>>> I have bought a new laptop (core i7, cpu 2.40 , 8G Ram) but I can't
>>> still run ems! it is 3 days that it is in the tuning step and it is not
>>> finished yet.
>>> Is it possible that it gets in an endless loop?
>>> How can I check it's process?
>>>
>>> regards
>>> Amir
>>>
>>> _______________________________________________
>>> Moses-support mailing list
>>> [email protected]
>>> http://mailman.mit.edu/mailman/listinfo/moses-support
>>>
>>>
>>>
>>
>>
>> _______________________________________________
>> Moses-support mailing
>> [email protected]http://mailman.mit.edu/mailman/listinfo/moses-support
>>
>>
>>
>
>
> _______________________________________________
> Moses-support mailing
> [email protected]http://mailman.mit.edu/mailman/listinfo/moses-support
>
>
>
################################################
### CONFIGURATION FILE FOR AN SMT EXPERIMENT ###
################################################
[GENERAL]
### directory in which experiment is run
#
working-dir = /opt/working/ems3
# specification of the language pair
input-extension = En
output-extension = Fa
pair-extension = En-Fa
### directories that contain tools and data
#
# moses
moses-src-dir = /opt/tools/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 = $moses-src-dir/tools
#
# srilm
#srilm-dir = $moses-src-dir/srilm/bin/i686
#
# irstlm
irstlm-dir = /opt/tools/irstlm/bin
#
# randlm
#randlm-dir = $moses-src-dir/randlm/bin
#
# data
toy-data = /opt/dataset/mizan
### basic tools
#
# moses decoder
decoder = $moses-bin-dir/moses
# conversion of phrase table into binary on-disk format
ttable-binarizer = $moses-bin-dir/processPhraseTable
# conversion of rule table into binary on-disk format
#ttable-binarizer = "$moses-bin-dir/CreateOnDiskPt 1 1 5 100 2"
# 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
### 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:toy]
### command to run to get raw corpus files
#
# get-corpus-script =
### raw corpus files (untokenized, but sentence aligned)
#
raw-stem = $toy-data/M_Tr
### 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 =
#################################################################
# LANGUAGE MODEL TRAINING
[LM]
### tool to be used for language model training
# 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 $working-dir/tmp"
settings = "-s msb -p 0"
# order of the language model
order = 5
### tool to be used for training randomized language model from scratch
# (more commonly, a SRILM is trained)
#
#rlm-training = "$randlm-dir/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:toy]
### command to run to get raw corpus files
#
#get-corpus-script = ""
### raw corpus (untokenized)
#
raw-corpus = /opt/dataset/mizanforlm/Mizan_Fa.txt
### tokenized corpus files (may contain long sentences)
#
#tokenized-corpus =
### if corpus preparation should be skipped,
# point to the prepared language model
#
#lm = /opt/tools/lm2/M_FaforLm.blm.Fa
#################################################################
# INTERPOLATING LANGUAGE MODELS
[INTERPOLATED-LM]
# 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 =
#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 = $toy-data/M_Tr.$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
#
#training-options = ""
### 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 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"
### create a bilingual concordancer for the model
#
#biconcor = $moses-script-dir/ems/biconcor/biconcor
### 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"
### include word alignment in phrase table
#
#include-word-alignment-in-rules = yes
### sparse lexical features
#
#sparse-lexical-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-with-reused-weights =
#####################################################
### TUNING: finding good weights for model components
[TUNING]
#continue = true
### instead of tuning with this setting, old weights may be recycled
# specify here an old configuration file with matching weights
#
#weight-config = $toy-data/weight.ini
### 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 =
#
raw-input = $toy-data/M_dev.$input-extension
#tokenized-input =
#factorized-input =
#input =
#
#reference-sgm =
#
raw-reference = $toy-data/M_dev.$output-extension
#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 8"
### if tuning should be skipped, specify this here
# and also point to a configuration file that contains
# pointers to all relevant model files
#
#config =
#########################################################
## RECASER: restore case, this part only trains the model
[RECASING]
#decoder = $moses-bin-dir/moses
### training data
# raw input needs to be still tokenized,
# also also tokenized input may be specified
#
#tokenized = [LM:europarl:tokenized-corpus]
# recase-config =
#lm-training = $srilm-dir/ngram-count
#######################################################
## TRUECASER: train model to truecase corpora and input
[TRUECASER]
### script to train truecaser models
#
trainer = $moses-script-dir/recaser/train-truecaser.perl
### training data
# data on which truecaser is trained
# if no training data is specified, parallel corpus is used
#
# raw-stem =
# tokenized-stem =
### trained model
#
# truecase-model =
######################################################################
## EVALUATION: translating a test set using the tuned system and score it
[EVALUATION]
### additional flags for the filter script
#
#filter-settings = ""
### additional decoder settings
# switches for the Moses decoder
# common choices:
# "-threads N" for multi-threading
# "-mbr" for MBR decoding
# "-drop-unknown" for dropping unknown source words
# "-search-algorithm 1 -cube-pruning-pop-limit 5000 -s 5000" for cube pruning
#
decoder-settings = "-search-algorithm 1 -cube-pruning-pop-limit 5000 -s 5000"
### specify size of n-best list, if produced
#
#nbest = 100
### multiple reference translations
#
#multiref = yes
### prepare system output for scoring
# this may include detokenization and wrapping output in sgm
# (needed for nist-bleu, ter, meteor)
#
detokenizer = "$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
#ibm-bleu =
### TER: translation error rate (BBN metric) based on edit distance
# not yet integrated
#
# ter =
### METEOR: gives credit to stem / worknet synonym matches
# not yet integrated
#
# meteor =
### 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:test]
### input data
# input-sgm =
#
raw-input = $toy-data/M_Ts.$input-extension
# tokenized-input =
# factorized-input =
# input =
### reference data
# reference-sgm =
#
raw-reference = $toy-data/M_Ts.$output-extension
# 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
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