Hello, My problem is not solved yet:(.
I changed the test data several times, but every time it got the "segmentation fault" error! the reordering table of the training data set is not empty but for all of the test data sets, it is empty. could anybody help me? Regards Amir amir haghighi wrote: the file model/reordering-table.* is not empty but the file evaluation/*.filtered.*/reordering-table.1.* is! my test set is not empty. thank you for your answers. On Sun, Dec 8, 2013 at 3:29 PM, Hieu Hoang < [email protected]> wrote: > everything looks ok, I'm not sure why it's segfaulting > > is the file > model/reordering-table.* > empty? If it is, then you should look in the log file > steps/*/TRAINING_build-reordering.*.STDERR > > or is > evaluation/*.filtered.*/reordering-table.1.* > empty? is your test set empty? > > > > On 8 December 2013 09:47, amir haghighi <amir.haghighi.64 <at> > gmail.com<[email protected]> > > wrote: > >> yes, the parallel data is UTF8.(one is UTF8 and one is ascii). >> all of the pre-processioning steps are done with moses scripts. >> >> here is the EMS config file content: >> >> ################################################ >> ### CONFIGURATION FILE FOR AN SMT EXPERIMENT ### >> ################################################ >> >> [GENERAL] >> >> ### directory in which experiment is run >> # >> working-dir = /opt/tools/workingEms >> >> # specification of the language pair >> input-extension = En >> output-extension = Fa >> pair-extension = En-Fa >> >> ### directories that contain tools and data >> # >> # moses >> moses-src-dir = >> /opt/tools/mosesdecoder-RELEASE-1.0/mosesdecoder-RELEASE-1.0 >> # >> # 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 = $moses-src-dir/tools >> # >> # srilm >> #srilm-dir = $moses-src-dir/srilm/bin/i686 >> # >> # irstlm >> irstlm-dir = /opt/tools/irstlm/bin >> # >> # randlm >> #randlm-dir = $moses-src-dir/randlm/bin >> # >> # data >> toy-data = /opt/tools/dataset/mizan >> >> ### 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/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/M_Tr >> >> ### 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 >> # 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:toy] >> >> ### command to run to get raw corpus files >> # >> #get-corpus-script = "" >> >> ### raw corpus (untokenized) >> # >> raw-corpus = $toy-data/M_Tr.$output-extension >> >> ### tokenized corpus files (may contain long sentences) >> # >> #tokenized-corpus = >> >> ### if corpus preparation should be skipped, >> # point to the prepared language model >> # >> lm = /opt/tools/lm2/M_FaforLm.blm.Fa >> >> ################################################################# >> # INTERPOLATING LANGUAGE MODELS >> >> [INTERPOLATED-LM] >> >> # if multiple language models are used, these may be combined >> # by optimizing perplexity on a tuning set >> # see, for instance [Koehn and Schwenk, IJCNLP 2008] >> >> ### script to interpolate language models >> # if commented out, no interpolation is performed >> # >> # script = $moses-script-dir/ems/support/interpolate-lm.perl >> >> ### tuning set >> # you may use the same set that is used for mert tuning (reference set) >> # >> #tuning-sgm = >> #raw-tuning = >> #tokenized-tuning = >> #factored-tuning = >> #lowercased-tuning = >> #split-tuning = >> >> ### group language models for hierarchical interpolation >> # (flat interpolation is limited to 10 language models) >> #group = "first,second fourth,fifth" >> >> ### script to use for binary table format for irstlm or kenlm >> # (default: no binarization) >> >> # irstlm >> #lm-binarizer = $irstlm-dir/compile-lm >> >> # kenlm, also set type to 8 >> #lm-binarizer = $moses-bin-dir/build_binary >> type = 8 >> >> ### script to create quantized language model format (irstlm) >> # (default: no quantization) >> # >> #lm-quantizer = $irstlm-dir/quantize-lm >> >> ### script to use for converting into randomized table format >> # (default: no randomization) >> # >> #lm-randomizer = "$randlm-dir/buildlm -falsepos 8 -values 8" >> >> ################################################################# >> # MODIFIED MOORE LEWIS FILTERING >> >> [MML] 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 = $toy-data/M_Tr.$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 >> # >> #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" >> >> ### parallelization of data preparation step >> # the two directions of the data preparation can be run in parallel >> # comment out if not needed >> # >> parallel = yes >> >> ### pre-computation for giza++ >> # giza++ has a more efficient data structure that needs to be >> # initialized with snt2cooc. if run in parallel, this may reduces >> # memory requirements. set here the number of parts >> # >> #run-giza-in-parts = 5 >> >> ### symmetrization method to obtain word alignments from giza output >> # (commonly used: grow-diag-final-and) >> # >> alignment-symmetrization-method = grow-diag-final-and >> >> ### use of berkeley aligner for word alignment >> # >> #use-berkeley = true >> #alignment-symmetrization-method = berkeley >> #berkeley-train = $moses-script-dir/ems/support/berkeley-train.sh >> #berkeley-process = $moses-script-dir/ems/support/berkeley-process.sh >> #berkeley-jar = /your/path/to/berkeleyaligner-1.1/berkeleyaligner.jar >> #berkeley-java-options = "-server -mx30000m -ea" >> #berkeley-training-options = "-Main.iters 5 5 -EMWordAligner.numThreads 8" >> #berkeley-process-options = "-EMWordAligner.numThreads 8" >> #berkeley-posterior = 0.5 >> >> ### use of baseline alignment model (incremental training) >> # >> #baseline = 68 >> #baseline-alignment-model = >> "$working-dir/training/prepared.$baseline/$input-extension.vcb \ >> # $working-dir/training/prepared.$baseline/$output-extension.vcb \ >> # >> $working-dir/training/giza.$baseline/${output-extension}-$input-extension.cooc >> \ >> # >> $working-dir/training/giza-inverse.$baseline/${input-extension}-$output-extension.cooc >> \ >> # >> $working-dir/training/giza.$baseline/${output-extension}-$input-extension.thmm.5 >> \ >> # >> $working-dir/training/giza.$baseline/${output-extension}-$input-extension.hhmm.5 >> \ >> # >> $working-dir/training/giza-inverse.$baseline/${input-extension}-$output-extension.thmm.5 >> \ >> # >> $working-dir/training/giza-inverse.$baseline/${input-extension}-$output-extension.hhmm.5" >> >> ### if word alignment should be skipped, >> # point to word alignment files >> # >> #word-alignment = $working-dir/model/aligned.1 >> >> ### filtering some corpora with modified Moore-Lewis >> # specify corpora to be filtered and ratio to be kept, either before or >> after word alignment >> #mml-filter-corpora = toy >> #mml-before-wa = "-proportion 0.9" >> #mml-after-wa = "-proportion 0.9" >> >> ### 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 = "" >> 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" >> >> ### include word alignment in phrase table >> # >> #include-word-alignment-in-rules = yes >> >> ### sparse lexical features >> # >> #sparse-lexical-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 = $toy-data/weight.ini >> >> ### 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 = >> #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-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 = $moses-script-dir/recaser/train-truecaser.perl >> >> ### training data >> # data on which truecaser is trained >> # if no training data is specified, parallel corpus is used >> # >> # raw-stem = >> # tokenized-stem = >> >> ### trained model >> # >> # truecase-model = >> >> ###################################################################### >> ## EVALUATION: translating a test set using the tuned system and score it >> >> [EVALUATION] >> >> ### additional flags for the filter script >> # >> #filter-settings = "" >> >> ### additional decoder settings >> # switches for the Moses decoder >> # common choices: >> # "-threads N" for multi-threading >> # "-mbr" for MBR decoding >> # "-drop-unknown" for dropping unknown source words >> # "-search-algorithm 1 -cube-pruning-pop-limit 5000 -s 5000" for cube >> pruning >> # >> decoder-settings = "-search-algorithm 1 -cube-pruning-pop-limit 5000 -s >> 5000" >> >> ### specify size of n-best list, if produced >> # >> #nbest = 100 >> >> ### multiple reference translations >> # >> #multiref = yes >> >> ### prepare system output for scoring >> # this may include detokenization and wrapping output in sgm >> # (needed for nist-bleu, ter, meteor) >> # >> detokenizer = "$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 >> # >> # visualization of the search graph in tree-based models >> #analyze-search-graph = yes >> >> [EVALUATION:test] >> >> ### input data >> # >> input-sgm = $toy-data/M_Ts.$input-extension >> # raw-input = >> # tokenized-input = >> # factorized-input = >> # input = >> >> ### reference data >> # >> reference-sgm = $toy-data/M_Ts.$output-extension >> # 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 >> >>> >>> >> >> On Sat, Dec 7, 2013 at 7:21 PM, Hieu Hoang < >> [email protected]> wrote: >> >>> are you sure the parallel data is encoded in UTF8? Was it tokenized, >>> cleaned and escaped by the Moses scripts or by another external script? >>> >>> Can you please send me you EMS config file too >>> >>> >>> On 7 December 2013 14:03, amir haghighi <amir.haghighi.64 <at> >>> gmail.com<[email protected]> >>> > wrote: >>> >>>> Hi, >>>> >>>> I have also the same problem in evaluation step with EMS and I would be >>>> thankful if you could help me. >>>> the lexical reordering file is emtpy and the log of the output in >>>> evaluation_test_filter.2.stderr is: >>>> >>>> Using SCRIPTS_ROOTDIR: >>>> /opt/tools/mosesdecoder-RELEASE-1.0/mosesdecoder-RELEASE-1.0/scripts >>>> (9) create moses.ini <at> Sat Dec 7 04:50:15 PST 2013 >>>> Executing: mkdir -p /opt/tools/workingEms/evaluation/test.filtered.2 >>>> Considering factor 0 >>>> Considering factor 0 >>>> filtering /opt/tools/workingEms/model/phrase-table.2 -> >>>> /opt/tools/workingEms/evaluation/test.filtered.2/phrase-table.0-0.1.1... >>>> 0 of 2197240 phrases pairs used (0.00%) - note: max length 10 >>>> binarizing...cat >>>> /opt/tools/workingEms/evaluation/test.filtered.2/phrase-table.0-0.1.1 | >>>> LC_ALL=C sort -T /opt/tools/workingEms/evaluation/test.filtered.2 | >>>> >>>> /opt/tools/mosesdecoder-RELEASE-1.0/mosesdecoder-RELEASE-1.0/bin/processPhraseTable >>>> -ttable 0 0 - -nscores 5 -out >>>> /opt/tools/workingEms/evaluation/test.filtered.2/phrase-table.0-0.1.1 >>>> processing ptree for stdin >>>> Segmentation fault (core dumped) >>>> filtering >>>> >>>> /opt/tools/workingEms/model/reordering-table.2.wbe-msd-bidirectional-fe.gz >>>> -> >>>> >>>> /opt/tools/workingEms/evaluation/test.filtered.2/reordering-table.2.wbe-msd-bidirectional-fe... >>>> 0 of 2197240 phrases pairs used (0.00%) - note: max length 10 >>>> >>>> binarizing.../opt/tools/mosesdecoder-RELEASE-1.0/mosesdecoder-RELEASE-1.0/bin/processLexicalTable >>>> -in >>>> >>>> /opt/tools/workingEms/evaluation/test.filtered.2/reordering-table.2.wbe-msd-bidirectional-fe >>>> -out >>>> >>>> /opt/tools/workingEms/evaluation/test.filtered.2/reordering-table.2.wbe-msd-bidirectional-fe >>>> processLexicalTable v0.1 by Konrad Rawlik >>>> processing >>>> >>>> /opt/tools/workingEms/evaluation/test.filtered.2/reordering-table.2.wbe-msd-bidirectional-fe >>>> to >>>> >>>> /opt/tools/workingEms/evaluation/test.filtered.2/reordering-table.2.wbe-msd-bidirectional-fe.* >>>> ERROR: empty lexicalised reordering file >>>> >>>> >>>> >>>> Barry Haddow <bhaddow <at> ...> writes: >>>> >>>> > >>>> > Hi Irene >>>> > >>>> > > But the output is empty. And the errors are 1. segmentation fault >>>> > 2. error: empty lexicalized >>>> > > reordering file >>>> > >>>> > Is this lexicalised reordering file empty then? >>>> > >>>> > It would be helpful if you could post the full log of the output when >>>> > your run the filter command, >>>> > >>>> > cheers - Barry >>>> > >>>> > On 26/10/12 17:59, Irene Huang wrote: >>>> > > Hi, I have trained and tuned the model, now I am using >>>> > > >>>> > > ~/mosesdecoder/scripts/training/filter-model-given-input.pl >>>> > > <http://filter-model-given-input.pl> filtered-newstest2011 >>>> > > mert-work/moses.ini ~/corpus/newstest2011.true.fr >>>> > > <http://newstest2011.true.fr> \ >>>> > > -Binarizer ~/mosesdecoder/bin/processPhraseTable >>>> > > >>>> > > to filter the phrase table. >>>> > > >>>> > > But the output is empty. And the errors are 1. segmentation fault >>>> > > 2. error: empty lexicalized reordering file >>>> > > >>>> > > So does this mean it's out of memory error? >>>> > > >>>> > > Thanks >>>> > > >>>> > > >>>> > > _______________________________________________ >>>> > > Moses-support mailing list >>>> > > Moses-support <at> ... >>>> > > http://mailman.mit.edu/mailman/listinfo/moses-support >>>> > >>>> >>>> >>>> >>>> >>>> _______________________________________________ >>>> Moses-support mailing list >>>> Moses-support <at> mit.edu <[email protected]> >>>> http://mailman.mit.edu/mailman/listinfo/moses-support >>>> >>>> >>> >>> >>> -- >>> Hieu Hoang >>> Research Associate >>> University of Edinburgh >>> http://www.hoang.co.uk/hieu >>> >>> >> >> _______________________________________________ >> Moses-support mailing list >> Moses-support <at> mit.edu <[email protected]> >> http://mailman.mit.edu/mailman/listinfo/moses-support >> >> > > > -- > Hieu Hoang > Research Associate > University of Edinburgh > http://www.hoang.co.uk/hieu > > _______________________________________________ Moses-support mailing listMoses-support@... <http://gmane.org/get-address.php?address=Moses%2dsupport%2d3s7WtUTddSA%40public.gmane.org>http://mailman.mit.edu/mailman/listinfo/moses-support
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