################################################
### CONFIGURATION FILE FOR AN SMT EXPERIMENT ###
################################################

[GENERAL]

thisFile = /home/nlp/Downloads/SMALL

### directory in which experiment is run
#
working-dir = $thisFile/
opt-dir = /home/nlp/opt

# specification of the language pair
input-extension = pl
output-extension = en
pair-extension = pl-en

### directories that contain tools and data
#
# moses
moses-src-dir = $opt-dir/moses
#
# 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 = $opt-dir/mgizapp/bin
#
# srilm
srilm-dir = $opt-dir/srilm/bin/i686-m64
#
# irstlm
#irstlm-dir = $opt-dir/irstlm/bin
#
# randlm
#randlm-dir = $opt-dir/randlm/bin
#
# data
wmt12-data = $thisFile

### basic tools
#
# moses decoder
decoder = $moses-bin-dir/moses

# conversion of phrase table into binary on-disk format
#ttable-binarizer = $moses-bin-dir/processPhraseTable
filter-settings = "-MinScore 2:0.0001,0:0.0001"

# conversion of rule table into binary on-disk format
ttable-binarizer = "$moses-bin-dir/CreateOnDiskPt 1 1 4 100 2"

input-tokenizer = "/home/nlp/opt/moses/scripts/tokenizer/normalize-punctuation.perl $input-extension | $moses-script-dir/tokenizer/tokenizer.perl -a -l $input-extension"
output-tokenizer = "/home/nlp/opt/moses/scripts/tokenizer/normalize-punctuation.perl $output-extension | $moses-script-dir/tokenizer/tokenizer.perl -a -l $output-extension"
#input-truecaser = /$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 = 2
#input-splitter = $moses-script-dir/generic/compound-splitter.perl


#################################################################
### 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:tags]

### command to run to get raw corpus files
#
# get-corpus-script =

### raw corpus files (untokenized, but sentence aligned)
#
raw-stem = $wmt12-data/train

### 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:inne]

### command to run to get raw corpus files
#
# get-corpus-script =

### raw corpus files (untokenized, but sentence aligned)
#
#raw-stem = $wmt12-data/inne



#################################################################
### 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 = 3

### 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:traintags]

### command to run to get raw corpus files
#
#get-corpus-script = ""

### raw corpus (untokenized)
#
raw-corpus = $wmt12-data/lm.$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:trainfr]
#raw-corpus = $wmt12-data/trainfr.$output-extension


#[LM:traintags=pos]
#factors = "pos"
#order = 6
#settings = "-interpolate -unk"
#raw-corpus = $wmt12-data/trainbigfr.$output-extension



#################################################################
### FACTOR DEFINITION
#################################################################

#[INPUT-FACTOR]

# also used for output factors
#temp-dir = $working-dir/training/factor


#[OUTPUT-FACTOR:pos]

### script that generates this factor
#
#mxpost = $opt-dir/moses/contrib/jmx
#factor-script = "$moses-script-dir/training/wrappers/make-factor-en-pos.mxpost.perl -mxpost -mx8096m $mxpost"



#################################################################
### 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 = $wmt12-data/dev.$pair-extension.$output-extension.xml
#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]

### 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
#type = 8

### in-/out-of-domain source/target corpora to train the 4 language model
#
#in-domain = [CORPUS:tags]
#outdomain-stem = [CORPUS:inne]

# ... or to two separate monolingual corpora
#indomain-target = [LM:toy:lowercased-corpus]
#raw-indomain-source = $wmt12-data/train.pl
#raw-indomain-target = $wmt12-data/train.en

# point to out-of-domain parallel corpus
#outdomain-stem = $wmt12-data/inne

# 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 = "-mgiza -mgiza-cpus $cores -sort-buffer-size 2G -sort-compress gzip -sort-parallel $cores -cores $cores"

### factored training: specify here which factors used
# if none specified, single factor training is assumed
# (one translation step, surface to surface)
#
#input-factors = word
#output-factors = word pos
#alignment-factors = "word -> word"
#translation-factors = "word -> word+pos"
#reordering-factors = "word -> word"
#generation-factors =
#decoding-steps = "t0"

### 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 = inne
#mml-before-wa = "-proportion 0.8"
#mml-after-wa = "-proportion 0.8"

### 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

#operation-sequence-model = "yes"
#operation-sequence-model-order = 5
#operation-sequence-model-settings = ""

### hierarchical rule set
#
#hierarchical-rule-set = true

domain-features = "sparse indicator"

### 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-lexical-features = "target-word-insertion all, source-word-deletion all, word-translation all, phrase-length"

#"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]

### 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-src-dir/bin --batch-mira --return-best-dev -maximum-iterations 2"

### specify the corpus used for tuning
# it should contain 1000s of sentences
#
input-sgm = $wmt12-data/dev.$pair-extension.$input-extension.xml
#raw-input =
#tokenized-input =
#factorized-input =
#input =
#
reference-sgm = $wmt12-data/dev.$pair-extension.$output-extension.xml
#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 $cores -mp -search-algorithm 1 -cube-pruning-pop-limit 1000 -s 1000 -feature-overwrite 'TranslationModel0 table-limit=100' -max-trans-opt-per-coverage 100"

### 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:traintags: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]

### 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 = "-mbr -threads $cores -mp -search-algorithm 1 -cube-pruning-pop-limit 5000 -s 5000 -feature-overwrite 'TranslationModel0 table-limit=100' -max-trans-opt-per-coverage 100"
#
### 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:traintags]

### input data
#
input-sgm = $wmt12-data/test.$pair-extension.$input-extension.xml
# raw-input =
# tokenized-input =
# factorized-input =
# input =

### reference data
#
reference-sgm = $wmt12-data/test.$pair-extension.$output-extension.xml
# 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