Concordance

Query N04-1022 18 >
Negative filter NN 14 (652.7 per million)

tech,2-1-N04-1022,bq	<term> EuroWordNet </term> . We present <term>	Minimum Bayes-Risk ( MBR ) decoding	</term> for <term> statistical machine translation	#6545 We presentMinimum Bayes-Risk ( MBR ) decoding for statistical machine translation.
tech,9-1-N04-1022,bq	Bayes-Risk ( MBR ) decoding </term> for <term>	statistical machine translation	</term> . This statistical approach aims	#6552 We present Minimum Bayes-Risk (MBR) decoding forstatistical machine translation.
other,6-2-N04-1022,bq	statistical approach aims to minimize <term>	expected loss of translation errors	</term> under <term> loss functions </term> that	#6562 This statistical approach aims to minimizeexpected loss of translation errors under loss functions that measure translation performance.
other,12-2-N04-1022,bq	of translation errors </term> under <term>	loss functions	</term> that measure <term> translation performance	#6568 This statistical approach aims to minimize expected loss of translation errors underloss functions that measure translation performance.
other,5-3-N04-1022,bq	</term> . We describe a hierarchy of <term>	loss functions	</term> that incorporate different levels	#6580 We describe a hierarchy ofloss functions that incorporate different levels of linguistic information from word strings, word-to-word alignments from an MT system, and syntactic structure from parse-trees of source and target language sentences.
other,12-3-N04-1022,bq	that incorporate different levels of <term>	linguistic information	</term> from <term> word strings </term> , <term>	#6587 We describe a hierarchy of loss functions that incorporate different levels oflinguistic information from word strings, word-to-word alignments from an MT system, and syntactic structure from parse-trees of source and target language sentences.
other,15-3-N04-1022,bq	<term> linguistic information </term> from <term>	word strings	</term> , <term> word-to-word alignments </term>	#6590 We describe a hierarchy of loss functions that incorporate different levels of linguistic information fromword strings, word-to-word alignments from an MT system, and syntactic structure from parse-trees of source and target language sentences.
other,18-3-N04-1022,bq	</term> from <term> word strings </term> , <term>	word-to-word alignments	</term> from an <term> MT system </term> , and	#6593 We describe a hierarchy of loss functions that incorporate different levels of linguistic information from word strings,word-to-word alignments from an MT system, and syntactic structure from parse-trees of source and target language sentences.
other,29-3-N04-1022,bq	<term> syntactic structure </term> from <term>	parse-trees	</term> of <term> source and target language	#6604 We describe a hierarchy of loss functions that incorporate different levels of linguistic information from word strings, word-to-word alignments from an MT system, and syntactic structure fromparse-trees of source and target language sentences.
other,31-3-N04-1022,bq	</term> from <term> parse-trees </term> of <term>	source and target language sentences	</term> . We report the performance of the	#6606 We describe a hierarchy of loss functions that incorporate different levels of linguistic information from word strings, word-to-word alignments from an MT system, and syntactic structure from parse-trees ofsource and target language sentences.
tech,6-4-N04-1022,bq	. We report the performance of the <term>	MBR decoders	</term> on a <term> Chinese-to-English translation	#6618 We report the performance of theMBR decoders on a Chinese-to-English translation task.
other,10-4-N04-1022,bq	of the <term> MBR decoders </term> on a <term>	Chinese-to-English translation task	</term> . Our results show that <term> MBR	#6622 We report the performance of the MBR decoders on aChinese-to-English translation task.
tech,11-5-N04-1022,bq	decoding </term> can be used to tune <term>	statistical MT	</term> performance for specific <term> loss	#6637 Our results show that MBR decoding can be used to tunestatistical MT performance for specific loss functions.
other,16-5-N04-1022,bq	MT </term> performance for specific <term>	loss functions	</term> . <term> CriterionSM Online Essay Evaluation	#6642 Our results show that MBR decoding can be used to tune statistical MT performance for specificloss functions.


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