| ################################################ |
| ### CONFIGURATION FILE FOR AN SMT EXPERIMENT ### |
| ################################################ |
|
|
| [GENERAL] |
|
|
| ### directory in which experiment is run |
| # |
| working-dir = WORKDIR/ems_workdir |
|
|
| # Giza and friends |
| external-bin-dir = WORKDIR/giza-pp/bin/ |
|
|
| # 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 = WORKDIR |
| # |
| # moses scripts |
| moses-script-dir = WORKDIR/scripts |
| # |
| # srilm |
| srilm-dir = SRILMDIR/bin/MACHINE_TYPE |
| # |
| # data |
| toy-data = $moses-script-dir/ems/example/data |
|
|
| ### basic tools |
| # |
| # moses decoder |
| decoder = $moses-src-dir/bin/moses |
|
|
| # conversion of phrase table into binary on-disk format |
| ttable-binarizer = $moses-src-dir/bin/processPhraseTable |
|
|
| # conversion of rule table into binary on-disk format |
| #ttable-binarizer = "$moses-src-dir/CreateOnDisk/src/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/nc-5k |
|
|
| ### 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 |
| # for instance: ngram-count (SRILM), train-lm-on-disk.perl (Edinburgh) |
| # |
| lm-training = $srilm-dir/ngram-count |
| settings = "-interpolate -kndiscount -unk" |
| order = 5 |
|
|
| ### tool to be used for training randomized language model from scratch |
| # (more commonly, a SRILM is trained) |
| # |
| #rlm-training = "$moses-src-dir/randlm/bin/buildlm -falsepos 8 -values 8" |
|
|
| ### script to use for binary table format for irstlm or kenlm |
| # (default: no binarization) |
|
|
| # irstlm |
| #lm-binarizer = $moses-src-dir/irstlm/bin/compile-lm |
|
|
| # kenlm, also set type to 8 |
| #lm-binarizer = $moses-src-dir/kenlm/build_binary |
| type = 8 |
|
|
| ### script to create quantized language model format (irstlm) |
| # (default: no quantization) |
| # |
| #lm-quantizer = $moses-src-dir/irstlm/bin/quantize-lm |
|
|
| ### script to use for converting into randomized table format |
| # (default: no randomization) |
| # |
| #lm-randomizer = "$moses-src-dir/randlm/bin/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 = "" |
|
|
| type = 8 |
|
|
| ### raw corpus (untokenized) |
| # |
| raw-corpus = $toy-data/nc-5k.$output-extension |
|
|
| ### tokenized corpus files (may contain long sentences) |
| # |
| #tokenized-corpus = |
|
|
| ### if corpus preparation should be skipped, |
| # point to the prepared language model |
| # |
| #lm = |
|
|
|
|
| [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 |
| # |
| #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" |
|
|
| ### 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 |
|
|
| ### if word alignment should be skipped, |
| # point to word alignment files |
| # |
| #word-alignment = $working-dir/model/aligned.1 |
|
|
| ### 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 = "" |
|
|
| ### 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 |
|
|
| ### 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 = |
|
|
| ### if training should be skipped, |
| # point to a configuration file that contains |
| # pointers to all relevant model files |
| # |
| #config = |
|
|
| ##################################################### |
| ### TUNING: finding good weights for model components |
|
|
| [TUNING] |
|
|
| ### instead of tuning with this setting, old weights may be recycled |
| # specify here an old configuration file with matching weights |
| # |
| weight-config = $toy-data/weight.ini |
|
|
| ### tuning script to be used |
| # |
| tuning-script = $moses-script-dir/training/mert-moses.pl |
| tuning-settings = "-mertdir $moses-src-dir/mert" |
|
|
| ### specify the corpus used for tuning |
| # it should contain 1000s of sentences |
| # |
| #input-sgm = |
| #raw-input = |
| #tokenized-input = |
| #factorized-input = |
| #input = |
| # |
| #reference-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 = "" |
|
|
| ### 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-src-dir/moses-cmd/src/moses.1521.srilm |
|
|
| ### 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 = $moses-script-dir/recaser/train-truecaser.perl |
|
|
| ### training data |
| # data on which truecaser is trained |
| # if no training data is specified, parallel corpus is used |
| # |
| # raw-stem = |
| # tokenized-stem = |
|
|
| ### trained model |
| # |
| # truecase-model = |
|
|
| ###################################################################### |
| ## EVALUATION: translating a test set using the tuned system and score it |
|
|
| [EVALUATION] |
|
|
| ### additional flags for the filter script |
| # |
| #filter-settings = "" |
|
|
| ### additional decoder settings |
| # switches for the Moses decoder |
| # |
| decoder-settings = "-search-algorithm 1 -cube-pruning-pop-limit 5000 -s 5000" |
|
|
| ### specify size of n-best list, if produced |
| # |
| #nbest = 100 |
|
|
| ### multiple reference translations |
| # |
| #multiref = yes |
|
|
| ### prepare system output for scoring |
| # this may include detokenization and wrapping output in sgm |
| # (needed for nist-bleu, ter, meteor) |
| # |
| detokenizer = "$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 = $moses-script-dir/generic/mteval-v12.pl |
| nist-bleu-c = "$moses-script-dir/generic/mteval-v12.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 |
|
|
| [EVALUATION:test] |
|
|
| ### input data |
| # |
| input-sgm = $toy-data/test-src.$input-extension.sgm |
| # raw-input = |
| # tokenized-input = |
| # factorized-input = |
| # input = |
|
|
| ### reference data |
| # |
| reference-sgm = $toy-data/test-ref.$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 |
|
|
|
|
