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tech,4-1-H05-1095,bq	performance </term> . This paper presents a <term>	phrase-based statistical machine translation method	</term> , based on <term> non-contiguous phrases	#7341 This paper presents aphrase-based statistical machine translation method, based on non-contiguous phrases, i.e. phrases with gaps.
other,12-1-H05-1095,bq	translation method </term> , based on <term>	non-contiguous phrases	</term> , i.e. <term> phrases </term> with gaps	#7349 This paper presents a phrase-based statistical machine translation method, based onnon-contiguous phrases, i.e. phrases with gaps.
other,16-1-H05-1095,bq	non-contiguous phrases </term> , i.e. <term>	phrases	</term> with gaps . A <term> method </term> for	#7353 This paper presents a phrase-based statistical machine translation method, based on non-contiguous phrases, i.e.phrases with gaps.
tech,1-2-H05-1095,bq	i.e. <term> phrases </term> with gaps . A <term>	method	</term> for producing such <term> phrases </term>	#7358 Amethod for producing such phrases from a word-aligned corpora is proposed.
other,5-2-H05-1095,bq	<term> method </term> for producing such <term>	phrases	</term> from a <term> word-aligned corpora </term>	#7362 A method for producing suchphrases from a word-aligned corpora is proposed.
lr,8-2-H05-1095,bq	producing such <term> phrases </term> from a <term>	word-aligned corpora	</term> is proposed . A <term> statistical	#7365 A method for producing such phrases from aword-aligned corpora is proposed.
tech,1-3-H05-1095,bq	word-aligned corpora </term> is proposed . A <term>	statistical translation model	</term> is also presented that deals such	#7371 Astatistical translation model is also presented that deals such phrases, as well as a training method based on the maximization of translation accuracy, as measured with the NIST evaluation metric.
other,10-3-H05-1095,bq	</term> is also presented that deals such <term>	phrases	</term> , as well as a <term> training method	#7380 A statistical translation model is also presented that deals suchphrases, as well as a training method based on the maximization of translation accuracy, as measured with the NIST evaluation metric.
tech,16-3-H05-1095,bq	<term> phrases </term> , as well as a <term>	training method	</term> based on the maximization of <term>	#7386 A statistical translation model is also presented that deals such phrases, as well as atraining method based on the maximization of translation accuracy, as measured with the NIST evaluation metric.
measure(ment),23-3-H05-1095,bq	</term> based on the maximization of <term>	translation accuracy	</term> , as measured with the <term> NIST	#7393 A statistical translation model is also presented that deals such phrases, as well as a training method based on the maximization oftranslation accuracy, as measured with the NIST evaluation metric.
measure(ment),30-3-H05-1095,bq	accuracy </term> , as measured with the <term>	NIST evaluation metric	</term> . <term> Translations </term> are produced	#7400 A statistical translation model is also presented that deals such phrases, as well as a training method based on the maximization of translation accuracy, as measured with theNIST evaluation metric.
other,0-4-H05-1095,bq	<term> NIST evaluation metric </term> . <term>	Translations	</term> are produced by means of a <term> beam-search	#7404 A statistical translation model is also presented that deals such phrases, as well as a training method based on the maximization of translation accuracy, as measured with the NIST evaluation metric.Translations are produced by means of a beam-search decoder.
tech,7-4-H05-1095,bq	Translations </term> are produced by means of a <term>	beam-search decoder	</term> . Experimental results are presented	#7411 Translations are produced by means of abeam-search decoder.
tech,10-5-H05-1095,bq	that demonstrate how the proposed <term>	method	</term> allows to better generalize from	#7424 Experimental results are presented, that demonstrate how the proposedmethod allows to better generalize from the training data.
lr,17-5-H05-1095,bq	allows to better generalize from the <term>	training data	</term> . This paper investigates some <term>	#7431 Experimental results are presented, that demonstrate how the proposed method allows to better generalize from thetraining data.


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