mirror of
https://github.com/moses-smt/mosesdecoder.git
synced 2024-12-29 15:04:05 +03:00
648 lines
18 KiB
Plaintext
648 lines
18 KiB
Plaintext
################################################
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### CONFIGURATION FILE FOR AN SMT EXPERIMENT ###
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################################################
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[GENERAL]
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### directory in which experiment is run
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#
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working-dir = /home/pkoehn/experiment
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# specification of the language pair
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input-extension = fr
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output-extension = en
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pair-extension = fr-en
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### directories that contain tools and data
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#
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# moses
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moses-src-dir = /home/pkoehn/moses
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#
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# moses binaries
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moses-bin-dir = $moses-src-dir/bin
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#
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# moses scripts
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moses-script-dir = $moses-src-dir/scripts
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#
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# directory where GIZA++/MGIZA programs resides
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external-bin-dir = /Users/hieuhoang/workspace/bin/training-tools
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#
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# srilm
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srilm-dir = $moses-src-dir/srilm/bin/i686
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#
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# irstlm
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irstlm-dir = $moses-src-dir/irstlm/bin
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#
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# randlm
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randlm-dir = $moses-src-dir/randlm/bin
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#
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# data
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wmt12-data = $working-dir/data
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### basic tools
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#
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# moses decoder
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decoder = $moses-bin-dir/moses_chart
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# conversion of phrase table into binary on-disk format
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#ttable-binarizer = $moses-bin-dir/processPhraseTable
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# conversion of rule table into binary on-disk format
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ttable-binarizer = "$moses-bin-dir/CreateOnDiskPt 1 1 4 100 2"
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# tokenizers - comment out if all your data is already tokenized
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input-tokenizer = "$moses-script-dir/tokenizer/tokenizer.perl -a -l $input-extension"
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output-tokenizer = "$moses-script-dir/tokenizer/tokenizer.perl -a -l $output-extension"
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# truecasers - comment out if you do not use the truecaser
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input-truecaser = $moses-script-dir/recaser/truecase.perl
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output-truecaser = $moses-script-dir/recaser/truecase.perl
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detruecaser = $moses-script-dir/recaser/detruecase.perl
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### generic parallelizer for cluster and multi-core machines
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# you may specify a script that allows the parallel execution
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# parallizable steps (see meta file). you also need specify
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# the number of jobs (cluster) or cores (multicore)
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#
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#generic-parallelizer = $moses-script-dir/ems/support/generic-parallelizer.perl
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#generic-parallelizer = $moses-script-dir/ems/support/generic-multicore-parallelizer.perl
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### cluster settings (if run on a cluster machine)
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# number of jobs to be submitted in parallel
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#
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#jobs = 10
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# arguments to qsub when scheduling a job
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#qsub-settings = ""
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# project for priviledges and usage accounting
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#qsub-project = iccs_smt
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# memory and time
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#qsub-memory = 4
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#qsub-hours = 48
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### multi-core settings
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# when the generic parallelizer is used, the number of cores
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# specified here
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cores = 8
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#################################################################
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# PARALLEL CORPUS PREPARATION:
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# create a tokenized, sentence-aligned corpus, ready for training
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[CORPUS]
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### long sentences are filtered out, since they slow down GIZA++
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# and are a less reliable source of data. set here the maximum
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# length of a sentence
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#
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max-sentence-length = 80
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[CORPUS:europarl] IGNORE
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### command to run to get raw corpus files
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#
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# get-corpus-script =
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### raw corpus files (untokenized, but sentence aligned)
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#
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raw-stem = $wmt12-data/training/europarl-v7.$pair-extension
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### tokenized corpus files (may contain long sentences)
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#
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#tokenized-stem =
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### if sentence filtering should be skipped,
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# point to the clean training data
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#
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#clean-stem =
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### if corpus preparation should be skipped,
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# point to the prepared training data
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#
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#lowercased-stem =
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[CORPUS:nc]
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raw-stem = $wmt12-data/training/news-commentary-v7.$pair-extension
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[CORPUS:un] IGNORE
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raw-stem = $wmt12-data/training/undoc.2000.$pair-extension
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#################################################################
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# LANGUAGE MODEL TRAINING
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[LM]
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### tool to be used for language model training
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# srilm
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lm-training = $srilm-dir/ngram-count
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settings = "-interpolate -kndiscount -unk"
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# irstlm training
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# msb = modified kneser ney; p=0 no singleton pruning
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#lm-training = "$moses-script-dir/generic/trainlm-irst2.perl -cores $cores -irst-dir $irstlm-dir -temp-dir $working-dir/tmp"
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#settings = "-s msb -p 0"
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# order of the language model
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order = 5
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### tool to be used for training randomized language model from scratch
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# (more commonly, a SRILM is trained)
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#
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#rlm-training = "$randlm-dir/buildlm -falsepos 8 -values 8"
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### script to use for binary table format for irstlm or kenlm
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# (default: no binarization)
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# irstlm
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#lm-binarizer = $irstlm-dir/compile-lm
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# kenlm, also set type to 8
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lm-binarizer = $moses-bin-dir/build_binary
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type = 8
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### script to create quantized language model format (irstlm)
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# (default: no quantization)
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#
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#lm-quantizer = $irstlm-dir/quantize-lm
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### script to use for converting into randomized table format
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# (default: no randomization)
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#
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#lm-randomizer = "$randlm-dir/buildlm -falsepos 8 -values 8"
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### each language model to be used has its own section here
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[LM:europarl] IGNORE
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### command to run to get raw corpus files
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#
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#get-corpus-script = ""
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### raw corpus (untokenized)
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#
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raw-corpus = $wmt12-data/training/europarl-v7.$output-extension
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### tokenized corpus files (may contain long sentences)
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#
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#tokenized-corpus =
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### if corpus preparation should be skipped,
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# point to the prepared language model
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#
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#lm =
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[LM:nc]
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raw-corpus = $wmt12-data/training/news-commentary-v7.$pair-extension.$output-extension
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[LM:un] IGNORE
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raw-corpus = $wmt12-data/training/undoc.2000.$pair-extension.$output-extension
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[LM:news] IGNORE
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raw-corpus = $wmt12-data/training/news.$output-extension.shuffled
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#################################################################
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# INTERPOLATING LANGUAGE MODELS
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[INTERPOLATED-LM] IGNORE
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# if multiple language models are used, these may be combined
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# by optimizing perplexity on a tuning set
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# see, for instance [Koehn and Schwenk, IJCNLP 2008]
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### script to interpolate language models
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# if commented out, no interpolation is performed
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#
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script = $moses-script-dir/ems/support/interpolate-lm.perl
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### tuning set
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# you may use the same set that is used for mert tuning (reference set)
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#
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tuning-sgm = $wmt12-data/dev/newstest2010-ref.$output-extension.sgm
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#raw-tuning =
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#tokenized-tuning =
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#factored-tuning =
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#lowercased-tuning =
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#split-tuning =
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### group language models for hierarchical interpolation
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# (flat interpolation is limited to 10 language models)
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#group = "first,second fourth,fifth"
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### script to use for binary table format for irstlm or kenlm
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# (default: no binarization)
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# irstlm
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#lm-binarizer = $irstlm-dir/compile-lm
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# kenlm, also set type to 8
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lm-binarizer = $moses-bin-dir/build_binary
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type = 8
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### script to create quantized language model format (irstlm)
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# (default: no quantization)
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#
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#lm-quantizer = $irstlm-dir/quantize-lm
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### script to use for converting into randomized table format
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# (default: no randomization)
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#
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#lm-randomizer = "$randlm-dir/buildlm -falsepos 8 -values 8"
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#################################################################
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# MODIFIED MOORE LEWIS FILTERING
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[MML] IGNORE
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### specifications for language models to be trained
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#
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#lm-training = $srilm-dir/ngram-count
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#lm-settings = "-interpolate -kndiscount -unk"
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#lm-binarizer = $moses-src-dir/bin/build_binary
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#lm-query = $moses-src-dir/bin/query
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#order = 5
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### in-/out-of-domain source/target corpora to train the 4 language model
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#
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# in-domain: point either to a parallel corpus
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#outdomain-stem = [CORPUS:toy:clean-split-stem]
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# ... or to two separate monolingual corpora
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#indomain-target = [LM:toy:lowercased-corpus]
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#raw-indomain-source = $toy-data/nc-5k.$input-extension
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# point to out-of-domain parallel corpus
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#outdomain-stem = [CORPUS:giga:clean-split-stem]
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# settings: number of lines sampled from the corpora to train each language model on
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# (if used at all, should be small as a percentage of corpus)
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#settings = "--line-count 100000"
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#################################################################
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# TRANSLATION MODEL TRAINING
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[TRAINING]
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### training script to be used: either a legacy script or
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# current moses training script (default)
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#
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script = $moses-script-dir/training/train-model.perl
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### general options
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# these are options that are passed on to train-model.perl, for instance
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# * "-mgiza -mgiza-cpus 8" to use mgiza instead of giza
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# * "-sort-buffer-size 8G -sort-compress gzip" to reduce on-disk sorting
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# * "-sort-parallel 8 -cores 8" to speed up phrase table building
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#
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#training-options = ""
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### factored training: specify here which factors used
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# if none specified, single factor training is assumed
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# (one translation step, surface to surface)
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#
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#input-factors = word lemma pos morph
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#output-factors = word lemma pos
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#alignment-factors = "word -> word"
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#translation-factors = "word -> word"
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#reordering-factors = "word -> word"
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#generation-factors = "word -> pos"
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#decoding-steps = "t0, g0"
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### parallelization of data preparation step
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# the two directions of the data preparation can be run in parallel
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# comment out if not needed
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#
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parallel = yes
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### pre-computation for giza++
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# giza++ has a more efficient data structure that needs to be
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# initialized with snt2cooc. if run in parallel, this may reduces
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# memory requirements. set here the number of parts
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#
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#run-giza-in-parts = 5
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### symmetrization method to obtain word alignments from giza output
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# (commonly used: grow-diag-final-and)
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#
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alignment-symmetrization-method = grow-diag-final-and
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### use of Chris Dyer's fast align for word alignment
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#
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#fast-align-settings = "-d -o -v"
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### use of berkeley aligner for word alignment
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#
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#use-berkeley = true
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#alignment-symmetrization-method = berkeley
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#berkeley-train = $moses-script-dir/ems/support/berkeley-train.sh
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#berkeley-process = $moses-script-dir/ems/support/berkeley-process.sh
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#berkeley-jar = /your/path/to/berkeleyaligner-1.1/berkeleyaligner.jar
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#berkeley-java-options = "-server -mx30000m -ea"
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#berkeley-training-options = "-Main.iters 5 5 -EMWordAligner.numThreads 8"
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#berkeley-process-options = "-EMWordAligner.numThreads 8"
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#berkeley-posterior = 0.5
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### use of baseline alignment model (incremental training)
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#
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#baseline = 68
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#baseline-alignment-model = "$working-dir/training/prepared.$baseline/$input-extension.vcb \
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# $working-dir/training/prepared.$baseline/$output-extension.vcb \
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# $working-dir/training/giza.$baseline/${output-extension}-$input-extension.cooc \
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# $working-dir/training/giza-inverse.$baseline/${input-extension}-$output-extension.cooc \
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# $working-dir/training/giza.$baseline/${output-extension}-$input-extension.thmm.5 \
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# $working-dir/training/giza.$baseline/${output-extension}-$input-extension.hhmm.5 \
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# $working-dir/training/giza-inverse.$baseline/${input-extension}-$output-extension.thmm.5 \
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# $working-dir/training/giza-inverse.$baseline/${input-extension}-$output-extension.hhmm.5"
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### if word alignment should be skipped,
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# point to word alignment files
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#
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#word-alignment = $working-dir/model/aligned.1
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### filtering some corpora with modified Moore-Lewis
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# specify corpora to be filtered and ratio to be kept, either before or after word alignment
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#mml-filter-corpora = toy
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#mml-before-wa = "-proportion 0.9"
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#mml-after-wa = "-proportion 0.9"
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### create a bilingual concordancer for the model
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#
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#biconcor = $moses-bin-dir/biconcor
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### lexicalized reordering: specify orientation type
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# (default: only distance-based reordering model)
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#
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#lexicalized-reordering = msd-bidirectional-fe
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### hierarchical rule set
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#
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hierarchical-rule-set = true
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### settings for rule extraction
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#
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#extract-settings = ""
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### add extracted phrases from baseline model
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#
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#baseline-extract = $working-dir/model/extract.$baseline
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#
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# requires aligned parallel corpus for re-estimating lexical translation probabilities
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#baseline-corpus = $working-dir/training/corpus.$baseline
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#baseline-alignment = $working-dir/model/aligned.$baseline.$alignment-symmetrization-method
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### unknown word labels (target syntax only)
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# enables use of unknown word labels during decoding
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# label file is generated during rule extraction
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#
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#use-unknown-word-labels = true
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### if phrase extraction should be skipped,
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# point to stem for extract files
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#
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# extracted-phrases =
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### settings for rule scoring
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#
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score-settings = "--GoodTuring"
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### include word alignment in phrase table
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#
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#include-word-alignment-in-rules = yes
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### sparse lexical features
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#
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#sparse-features = "target-word-insertion top 50, source-word-deletion top 50, word-translation top 50 50, phrase-length"
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### domain adaptation settings
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# options: sparse, any of: indicator, subset, ratio
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#domain-features = "subset"
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### if phrase table training should be skipped,
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# point to phrase translation table
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#
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# phrase-translation-table =
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### if reordering table training should be skipped,
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# point to reordering table
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#
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# reordering-table =
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### filtering the phrase table based on significance tests
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# Johnson, Martin, Foster and Kuhn. (2007): "Improving Translation Quality by Discarding Most of the Phrasetable"
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# options: -n number of translations; -l 'a+e', 'a-e', or a positive real value -log prob threshold
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#salm-index = /path/to/project/salm/Bin/Linux/Index/IndexSA.O64
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#sigtest-filter = "-l a+e -n 50"
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### if training should be skipped,
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# point to a configuration file that contains
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# pointers to all relevant model files
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#
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#config-with-reused-weights =
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#####################################################
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### TUNING: finding good weights for model components
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[TUNING]
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### instead of tuning with this setting, old weights may be recycled
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# specify here an old configuration file with matching weights
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#
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#weight-config = $working-dir/tuning/moses.weight-reused.ini.1
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### tuning script to be used
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#
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tuning-script = $moses-script-dir/training/mert-moses.pl
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tuning-settings = "-mertdir $moses-bin-dir"
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### specify the corpus used for tuning
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# it should contain 1000s of sentences
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#
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input-sgm = $wmt12-data/dev/newstest2010-src.$input-extension.sgm
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#raw-input =
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#tokenized-input =
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#factorized-input =
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#input =
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#
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reference-sgm = $wmt12-data/dev/newstest2010-ref.$output-extension.sgm
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#raw-reference =
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#tokenized-reference =
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#factorized-reference =
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#reference =
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### size of n-best list used (typically 100)
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#
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nbest = 100
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### ranges for weights for random initialization
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# if not specified, the tuning script will use generic ranges
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# it is not clear, if this matters
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#
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# lambda =
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### additional flags for the filter script
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#
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filter-settings = ""
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### additional flags for the decoder
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#
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decoder-settings = ""
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### if tuning should be skipped, specify this here
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# and also point to a configuration file that contains
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# pointers to all relevant model files
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#
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#config =
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#########################################################
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## RECASER: restore case, this part only trains the model
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[RECASING]
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#decoder = $moses-bin-dir/moses
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### training data
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# raw input needs to be still tokenized,
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# also also tokenized input may be specified
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#
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#tokenized = [LM:europarl:tokenized-corpus]
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# recase-config =
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#lm-training = $srilm-dir/ngram-count
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#######################################################
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## TRUECASER: train model to truecase corpora and input
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[TRUECASER]
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### script to train truecaser models
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#
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trainer = $moses-script-dir/recaser/train-truecaser.perl
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### training data
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# data on which truecaser is trained
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# if no training data is specified, parallel corpus is used
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#
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# raw-stem =
|
|
# tokenized-stem =
|
|
|
|
### trained model
|
|
#
|
|
# truecase-model =
|
|
|
|
######################################################################
|
|
## EVALUATION: translating a test set using the tuned system and score it
|
|
|
|
[EVALUATION]
|
|
|
|
### number of jobs (if parallel execution on cluster)
|
|
#
|
|
#jobs = 10
|
|
|
|
### additional flags for the filter script
|
|
#
|
|
#filter-settings = ""
|
|
|
|
### additional decoder settings
|
|
# switches for the Moses decoder
|
|
# common choices:
|
|
# "-threads N" for multi-threading
|
|
# "-mbr" for MBR decoding
|
|
# "-drop-unknown" for dropping unknown source words
|
|
# "-search-algorithm 1 -cube-pruning-pop-limit 5000 -s 5000" for cube pruning
|
|
#
|
|
#decoder-settings = ""
|
|
|
|
### specify size of n-best list, if produced
|
|
#
|
|
#nbest = 100
|
|
|
|
### multiple reference translations
|
|
#
|
|
#multiref = yes
|
|
|
|
### prepare system output for scoring
|
|
# this may include detokenization and wrapping output in sgm
|
|
# (needed for nist-bleu, ter, meteor)
|
|
#
|
|
detokenizer = "$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: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
|
|
|