From 0ac66e310d57f9aea5ddeea900c84df08abfe8c2 Mon Sep 17 00:00:00 2001 From: Patrick Simianer Date: Fri, 27 Apr 2012 01:54:47 +0200 Subject: fix approx. BLEU of (Chiang et al. '08) --- dtrain/test/example/dtrain.ini | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) (limited to 'dtrain/test/example') diff --git a/dtrain/test/example/dtrain.ini b/dtrain/test/example/dtrain.ini index cd2c75e7..2ad44688 100644 --- a/dtrain/test/example/dtrain.ini +++ b/dtrain/test/example/dtrain.ini @@ -4,18 +4,18 @@ decoder_config=test/example/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 tmp=/tmp -stop_after=100 # stop epoch after 100 inputs +stop_after=20 # stop epoch after 20 inputs # interesting stuff epochs=3 # run over input 3 times k=100 # use 100best lists N=4 # optimize (approx) BLEU4 -scorer=approx_bleu # use 'stupid' BLEU+1 +scorer=stupid_bleu # use 'stupid' BLEU+1 learning_rate=0.0001 # learning rate 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 +hi_lo=0.1 # 10 vs 80 vs 10 and 80 vs 10 here pair_threshold=0 # minimum distance in BLEU (this will still only use pairs with diff > 0) select_weights=VOID # don't output weights -- cgit v1.2.3