input=./nc-wmt11.de.gz refs=./nc-wmt11.en.gz output=- # a weights file (add .gz for gzip compression) or STDOUT '-' select_weights=VOID # output average (over epochs) weight vector decoder_config=./cdec.ini # config for cdec # weights for these features will be printed on each iteration print_weights=Glue WordPenalty LanguageModel LanguageModel_OOV PhraseModel_0 PhraseModel_1 PhraseModel_2 PhraseModel_3 PhraseModel_4 PhraseModel_5 PhraseModel_6 PassThrough # newer version of the grammar extractor use different feature names: #print_weights= EgivenFCoherent SampleCountF CountEF MaxLexFgivenE MaxLexEgivenF IsSingletonF IsSingletonFE Glue WordPenalty PassThrough LanguageModel LanguageModel_OOV stop_after=10 # stop epoch after 10 inputs # interesting stuff epochs=2 # run over input 2 times k=100 # use 100best lists N=4 # optimize (approx) BLEU4 scorer=stupid_bleu # use 'stupid' BLEU+1 learning_rate=1.0 # learning rate, don't care if gamma=0 (perceptron) gamma=0 # use SVM reg sample_from=kbest # use kbest lists (as opposed to forest) filter=uniq # only unique entries in kbest (surface form) pair_sampling=XYX # hi_lo=0.1 # 10 vs 80 vs 10 and 80 vs 10 here pair_threshold=0 # minimum distance in BLEU (here: > 0) loss_margin=0 # update if correctly ranked, but within this margin