| ################################################ |
| ### CONFIGURATION FILE FOR AN SMT EXPERIMENT ### |
| ################################################ |
|
|
| [GENERAL] |
|
|
| ### directory in which experiment is run |
| # |
| working-dir = /home/pkoehn/experiment |
|
|
| # 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/pkoehn/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 = /Users/hieuhoang/workspace/bin/training-tools |
| # |
| # srilm |
| srilm-dir = $moses-src-dir/srilm/bin/i686 |
| # |
| # 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" |
|
|
| # 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 $output-extension" |
|
|
| # For Arabic tokenizer try Farasa (download: http: |
| # Abdelali, Darwish, Durrani, Mubarak (NAACL demo 2016) |
| # "Farasa: A Fast and Furious Segmenter for Arabic" |
| #input-tokenizer = "$farasa-dir/farasa_moses.sh" |
|
|
| # 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 = 4 |
|
|
| ################################################################# |
| # 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] IGNORE |
|
|
| ### 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 $working-dir/tmp" |
| #settings = "-s msb -p 0" |
|
|
| # order of the language model |
| order = 5 |
|
|
| ### tool to be used for training randomized language model from scratch |
| # (more commonly, a SRILM is trained) |
| # |
| #rlm-training = "$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:europarl] IGNORE |
|
|
| ### command to run to get raw corpus files |
| # |
| #get-corpus-script = "" |
|
|
| ### raw corpus (untokenized) |
| # |
| raw-corpus = $wmt12-data/training/europarl-v7.$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 |
|
|
| [LM:nc=pos] |
| factors = "pos" |
| order = 7 |
| settings = "-interpolate -unk" |
| raw-corpus = $wmt12-data/training/news-commentary-v7.$pair-extension.$output-extension |
|
|
| ################################################################# |
| # 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 = $wmt12-data/dev/newstest2010-ref.$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" |
|
|
| ################################################################# |
| # FACTOR DEFINITION |
|
|
| [INPUT-FACTOR] |
|
|
| # also used for output factors |
| temp-dir = $working-dir/training/factor |
|
|
| [OUTPUT-FACTOR:pos] |
|
|
| ### script that generates this factor |
| # |
| mxpost = /home/pkoehn/bin/mxpost |
| factor-script = "$moses-script-dir/training/wrappers/make-factor-en-pos.mxpost.perl -mxpost $mxpost" |
|
|
| ################################################################# |
| # 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 = "" |
|
|
| ### 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 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%'" |
| # |
| # OR if you want to use with SRILM |
| # |
| #operation-sequence-model-settings = "--srilm-dir /path-to-srilm/bin/i686-m64" |
| |
| ## Class-based Operation Sequence Model (OSM) |
| # if OSM has to be enabled with factors then add factors as below. |
| # Durrani, Koehn, Schmid, Fraser (COLING, 2014). |
| #Investigating the Usefulness of Generalized Word Representations in SMT |
| # |
| #operation-sequence-model-settings = "--factor 0-0+1-1" |
| |
| ## Interpolated Operation Sequence Model (OSM) |
| # if OSM has to be enabled with factors then add factors as below. |
| # Durrani, Sajjad, Joty, Abdelali and Vogel (Mt Summit, 2015). |
| # Using Joint Models for Domain Adaptation in Statistical Machine Translation |
| # |
| #interpolated-operation-sequence-model = "yes" |
| #operation-sequence-model-order = 5 |
| #operation-sequence-model-settings = "--srilm-dir /path-to-srilm/bin/i686-m64 --tune /path-to-tune-folder/tune_file" |
| #Interpolated OSM can only be used with SRILM because of the interpolation script |
| |
| |
| # 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-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-bin-dir" |
| |
| ### specify the corpus used for tuning |
| # it should contain 1000s of sentences |
| # |
| input-sgm = $wmt12-data/dev/newstest2010-src.$input-extension.sgm |
| #raw-input = |
| #tokenized-input = |
| #factorized-input = |
| #input = |
| # |
| reference-sgm = $wmt12-data/dev/newstest2010-ref.$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 $cores" |
| |
| ### 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] IGNORE |
| |
| ### 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 -threads $cores" |
| |
| ### specify size of n-best list, if produced |
| # |
| #nbest = 100 |
| |
| ### multiple reference translations |
| # |
| #multiref = yes |
| |
| ### prepare system output for scoring |
| # this may include detokenization and wrapping output in sgm |
| # (needed for nist-bleu, ter, meteor) |
| # |
| detokenizer = "$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 = |
| #sacre-bleu = "sacrebleu -lc" |
| #sacre-bleu-c = "sacrebleu" |
| |
| ### 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 |
| |
| |
