Στις 30/05/2012 12:41 πμ, ο/η Δημήτρης Μπαμπανιώτης έγραψε: > Στις 28/05/2012 10:01 μμ, ο/η Philipp Koehn έγραψε: >> Hi, >> >> there is a problem here: >> >> # conversion of phrase table into binary on-disk format >> #ttable-binarizer = $moses-bin-dir/processPhraseTable >> >> # conversion of rule table into binary on-disk format >> ttable-binarizer = "$moses-bin-dir/CreateOnDisk 1 1 5 100 2" >> >> You are using the ttable binarizer for the hierarchical/syntax model, >> but you use a phrase-based model. >> >> -phi >> >> On Sun, May 27, 2012 at 11:45 PM, Dimitris Babaniotis >> <[email protected]> wrote: >>> Hello, I'm trying to run experiments with EMS but the process stops on >>> tuning:tune. >>> >>> Here is the TUNING_tune.stderr file : >>> >>> main::create_extractor_script() called too early to check prototype at >>> /home/dimbaba/moses/scripts/training/mert-moses.pl line 674. >>> Using SCRIPTS_ROOTDIR: /home/dimbaba/moses/scripts >>> Asking moses for feature names and values from >>> /home/dimbaba/mosesFactored/experiment/tuning/moses.filtered.ini.4 >>> Executing: /home/dimbaba/moses/dist/bin/moses -v 0 -config >>> /home/dimbaba/mosesFactored/experiment/tuning/moses.filtered.ini.4 >>> -inputtype 0 -show-weights> ./features.list >>> MERT starting values and ranges for random generation: >>> d = 0.600 ( 0.00 .. 1.00) >>> lm = 0.250 ( 0.00 .. 1.00) >>> lm = 0.250 ( 0.00 .. 1.00) >>> w = -1.000 ( 0.00 .. 1.00) >>> tm = 0.200 ( 0.00 .. 1.00) >>> tm = 0.200 ( 0.00 .. 1.00) >>> tm = 0.200 ( 0.00 .. 1.00) >>> tm = 0.200 ( 0.00 .. 1.00) >>> tm = 0.200 ( 0.00 .. 1.00) >>> Saved: ./run1.moses.ini >>> Normalizing lambdas: 0.600000 0.250000 0.250000 -1.000000 0.200000 >>> 0.200000 >>> 0.200000 0.200000 0.200000 >>> DECODER_CFG = -w -0.322581 -lm 0.080645 0.080645 -d 0.193548 -tm >>> 0.064516 >>> 0.064516 0.064516 0.064516 0.064516 >>> Executing: /home/dimbaba/moses/dist/bin/moses -v 0 -config >>> /home/dimbaba/mosesFactored/experiment/tuning/moses.filtered.ini.4 >>> -inputtype 0 -w -0.322581 -lm 0.080645 0.080645 -d 0.193548 -tm >>> 0.064516 >>> 0.064516 0.064516 0.064516 0.064516 -n-best-list run1.best100.out 100 >>> -input-file /home/dimbaba/mosesFactored/experiment/tuning/input.tc.1> >>> run1.out >>> Translating line 0 in thread id 140471666632448 >>> Check (*contextFactor[count-1])[factorType] != NULL failed in >>> moses/src/LM/SRI.cpp:155 >>> sh: line 1: 1648 Ακυρώθηκε (core dumped) >>> /home/dimbaba/moses/dist/bin/moses >>> -v 0 -config >>> /home/dimbaba/mosesFactored/experiment/tuning/moses.filtered.ini.4 >>> -inputtype 0 -w -0.322581 -lm 0.080645 0.080645 -d 0.193548 -tm >>> 0.064516 >>> 0.064516 0.064516 0.064516 0.064516 -n-best-list run1.best100.out 100 >>> -input-file /home/dimbaba/mosesFactored/experiment/tuning/input.tc.1> >>> run1.out >>> Exit code: 134 >>> The decoder died. CONFIG WAS -w -0.322581 -lm 0.080645 0.080645 -d >>> 0.193548 >>> -tm 0.064516 0.064516 0.064516 0.064516 0.064516 >>> cp: cannot stat >>> «/home/dimbaba/mosesFactored/experiment/tuning/tmp.4/moses.ini»: Δεν >>> υπάρχει >>> τέτοιο αρχείο ή κατάλογος >>> >>> >>> ...and this is my configuration file: >>> >>> >>> ################################################ >>> ### CONFIGURATION FILE FOR AN SMT EXPERIMENT ### >>> ################################################ >>> >>> [GENERAL] >>> >>> ### directory in which experiment is run >>> # >>> working-dir = /home/dimbaba/mosesFactored/experiment >>> >>> # specification of the language pair >>> input-extension = de >>> output-extension = el >>> pair-extension = de-el >>> >>> ### directories that contain tools and data >>> # >>> # moses >>> moses-src-dir = /home/dimbaba/moses >>> # >>> # moses binaries >>> moses-bin-dir = $moses-src-dir/dist/bin >>> # >>> # moses scripts >>> moses-script-dir = $moses-src-dir/scripts >>> # >>> # srilm >>> srilm-dir = /home/dimbaba/srilm/bin/i686-m64 >>> # >>> # irstlm >>> #irstlm-dir = $moses-src-dir/irstlm/bin >>> # >>> # randlm >>> #randlm-dir = $moses-src-dir/randlm/bin >>> # >>> # data >>> wmt12-data = /home/dimbaba/aligned/el-de >>> >>> ### basic tools >>> # >>> # moses decoder >>> decoder = $moses-bin-dir/moses >>> >>> # conversion of phrase table into binary on-disk format >>> #ttable-binarizer = $moses-bin-dir/processPhraseTable >>> >>> # conversion of rule table into binary on-disk format >>> ttable-binarizer = "$moses-bin-dir/CreateOnDisk 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 = 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 = 100 >>> >>> [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/training.clean10 >>> >>> ### 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/training.clean10 >>> >>> [CORPUS:un] IGNORE >>> raw-stem = $wmt12-data/training/training.clean10 >>> >>> ################################################################# >>> # LANGUAGE MODEL TRAINING >>> >>> [LM] >>> >>> ### tool to be used for language model training >>> # srilm >>> lm-training = $srilm-dir/ngram-count >>> settings = "" >>> >>> # irstlm >>> #lm-training = "$moses-script-dir/generic/trainlm-irst.perl -cores >>> $cores >>> -irst-dir $irstlm-dir -temp-dir $working-dir/lm" >>> #settings = "" >>> >>> # order of the language model >>> order = 3 >>> >>> ### tool to be used for training randomized language model from scratch >>> # (more commonly, a SRILM is trained) >>> # >>> #rlm-training = "$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:europarl] IGNORE >>> >>> ### command to run to get raw corpus files >>> # >>> #get-corpus-script = "" >>> >>> ### raw corpus (untokenized) >>> # >>> raw-corpus = $wmt12-data/training/training.clean.$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 = $wmt12-data/training/training.clean10.$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=stem] >>> factors = "stem" >>> order = 3 >>> settings = "" >>> raw-corpus = $wmt12-data/training/training.clean.$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 = $working-dir/training/factor >>> [INPUT-FACTOR:stem] >>> >>> factor-script = >>> "$moses-script-dir/training/wrappers/make-factor-stem.perl >>> 3" >>> ### script that generates this factor >>> # >>> #mxpost = /home/pkoehn/bin/mxpost >>> factor-script = >>> "$moses-script-dir/training/wrappers/make-factor-stem.perl >>> 3" >>> [OUTPUT-FACTOR:stem] >>> >>> factor-script = >>> "$moses-script-dir/training/wrappers/make-factor-stem.perl >>> 3" >>> ### script that generates this factor >>> # >>> #mxpost = /home/pkoehn/bin/mxpost >>> factor-script = >>> "$moses-script-dir/training/wrappers/make-factor-stem.perl >>> 3" >>> >>> ################################################################# >>> # 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" to reduce on-disk sorting >>> # >>> #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 stem >>> output-factors = word stem >>> alignment-factors = "stem -> stem" >>> translation-factors = "word -> word" >>> 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 >>> >>> ### 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-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.filtered.ini.1 >>> >>> ### tuning script to be used >>> # >>> tuning-script = $moses-script-dir/training/mert-moses.pl >>> tuning-settings = "-mertdir $moses-bin-dir --filtercmd >>> '$moses-script-dir/training/filter-model-given-input.pl'" >>> >>> ### specify the corpus used for tuning >>> # it should contain 1000s of sentences >>> # >>> #input-sgm = >>> raw-input = $wmt12-data/tuning/tuning.clean.$input-extension >>> #tokenized-input = >>> #factorized-input = >>> #input = >>> # >>> #reference-sgm = >>> raw-reference = $wmt12-data/tuning/tuning.clean.$output-extension >>> #tokenized-reference = >>> #factorized-reference = >>> #reference = >>> >>> ### size of n-best list used (typically 100) >>> # >>> nbest = 100 >>> >>> ### ranges for weights for random initialization >>> # if not specified, the tuning script will use generic ranges >>> # it is not clear, if this matters >>> # >>> # lambda = >>> >>> ### additional flags for the filter script >>> # >>> #filter-settings = "-Binarizer CreateOnDiskPt 1 1 5 100 2 >>> -Hierarchical" >>> >>> ### 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-bin-dir/moses >>> >>> ### training data >>> # raw input needs to be still tokenized, >>> # also also tokenized input may be specified >>> # >>> #tokenized = [LM:europarl:tokenized-corpus] >>> >>> # recase-config = >>> >>> #lm-training = $srilm-dir/ngram-count >>> >>> ####################################################### >>> ## TRUECASER: train model to truecase corpora and input >>> >>> [TRUECASER] >>> >>> ### script to train truecaser models >>> # >>> trainer = $moses-script-dir/recaser/train-truecaser.perl >>> >>> ### training data >>> # data on which truecaser is trained >>> # if no training data is specified, parallel corpus is used >>> # >>> # raw-stem = >>> # tokenized-stem = >>> >>> ### trained model >>> # >>> # truecase-model = >>> >>> ###################################################################### >>> ## EVALUATION: translating a test set using the tuned system and >>> score it >>> >>> [EVALUATION] >>> >>> ### number of jobs (if parallel execution on cluster) >>> # >>> #jobs = 10 >>> >>> ### additional flags for the filter script >>> # >>> #filter-settings = "" >>> >>> ### additional decoder settings >>> # switches for the Moses decoder >>> # common choices: >>> # "-threads N" for multi-threading >>> # "-mbr" for MBR decoding >>> # "-drop-unknown" for dropping unknown source words >>> # "-search-algorithm 1 -cube-pruning-pop-limit 5000 -s 5000" for cube >>> pruning >>> # >>> decoder-settings = "-search-algorithm 1 -cube-pruning-pop-limit 5000 -s >>> 5000" >>> >>> ### specify size of n-best list, if produced >>> # >>> #nbest = 100 >>> >>> ### multiple reference translations >>> # >>> #multiref = yes >>> >>> ### prepare system output for scoring >>> # this may include detokenization and wrapping output in sgm >>> # (needed for nist-bleu, ter, meteor) >>> # >>> detokenizer = "$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 = $moses-script-dir/ems/support/analysis.perl >>> # >>> # also report on input coverage >>> analyze-coverage = yes >>> # >>> # also report on phrase mappings used >>> report-segmentation = yes >>> # >>> # report precision of translations for each input word, broken down by >>> # count of input word in corpus and model >>> #report-precision-by-coverage = yes >>> # >>> # further precision breakdown by factor >>> #precision-by-coverage-factor = pos >>> >>> [EVALUATION:newstest2011] >>> >>> ### input data >>> # >>> #input-sgm = "$wmt12-data/$input-extension-test.txt" >>> #raw-input = $wmt12-data/$input-extension-test.txt >>> tokenized-input = "$wmt12-data/de-test.txt" >>> # factorized-input = >>> #input = $wmt12-data/$input-extension-test.txt >>> >>> ### reference data >>> # >>> #reference-sgm = "$wmt12-data/$output-extension-test.txt" >>> #raw-reference ="wmt12-data/$output-extension -test.txt >>> tokenized-reference = "$wmt12-data/el-test.txt" >>> #reference = $wmt12-data/el-test.txt >>> >>> ### 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 = $tokenized-input >>> >>> ########################################## >>> ### REPORTING: summarize evaluation scores >>> >>> [REPORTING] >>> >>> ### currently no parameters for reporting section >>> >>> Thank you, >>> >>> Dimitris Babaniotis >>> >>> _______________________________________________ >>> Moses-support mailing list >>> [email protected] >>> http://mailman.mit.edu/mailman/listinfo/moses-support >>> > Hi, thank you for your answer, > > I fixed the problem that you mentioned but the problem still exists. > > I searched more and i found that the error occurs when the decoder > tries to to translate a sentence. > The problem exists with or without EMS. > > Dimitris > Hi,
I have a new problem with the moses machine, when the tuning process finished all the weights were zero. Do you know what happened? Here is my configuration file from tuning: # MERT optimized configuration # decoder /home/dimbaba/mosesdecoder/dist/bin/moses # BLEU 0 on dev /home/dimbaba/mosesOnlySuffix/tuning.combined.de # We were before running iteration 2 # finished Τρι 05 Ιούν 2012 03:14:03 μμ EEST ### MOSES CONFIG FILE ### ######################### # input factors [input-factors] 0 1 # mapping steps [mapping] 0 T 0 0 T 1 # translation tables: table type (hierarchical(0), textual (0), binary (1)), source-factors, target-factors, number of scores, file # OLD FORMAT is still handled for back-compatibility # OLD FORMAT translation tables: source-factors, target-factors, number of scores, file # OLD FORMAT a binary table type (1) is assumed [ttable-file] 0 0 0 5 /home/dimbaba/mosesOnlySuffix/work/tuning/mert/filtered/phrase-table.0-0.1.1.gz 0 1 1 5 /home/dimbaba/mosesOnlySuffix/work/tuning/mert/filtered/phrase-table.1-1.1.1.gz # no generation models, no generation-file section # language models: type(srilm/irstlm), factors, order, file [lmodel-file] 0 1 3 /home/dimbaba/mosesOnlySuffix/factored.lm # limit on how many phrase translations e for each phrase f are loaded # 0 = all elements loaded [ttable-limit] 20 0 # distortion (reordering) weight [weight-d] 0 # language model weights [weight-l] 0 # translation model weights [weight-t] 0 0 0 0 0 0 0 0 0 0 # no generation models, no weight-generation section # word penalty [weight-w] 0 [distortion-limit] 6 Dimitris Babaniotis _______________________________________________ Moses-support mailing list [email protected] http://mailman.mit.edu/mailman/listinfo/moses-support
