Concordance

Query translation 96 (4,475.7 per million)

tech,10-1-H01-1041,bq	developing a <term> Korean-to-English machine	translation	system </term><term> CCLINC ( Common Coalition	#398 At MIT Lincoln Laboratory, we have been developing a Korean-to-English machine translation system CCLINC (Common Coalition Language System at Lincoln Laboratory).
tool,1-2-H01-1041,bq	</term> . The <term> CCLINC Korean-to-English	translation	system </term> consists of two <term> core	#414 The CCLINC Korean-to-English translation system consists of two core modules, language understanding and generation modules mediated by a language neutral meaning representation called a semantic frame.
tech,5-4-H01-1041,bq	arguments </term> ) . ( ii ) High quality <term>	translation	</term> via <term> word sense disambiguation	#482 (ii) High qualitytranslation via word sense disambiguation and accurate word order generation of the target language.
other,18-6-H01-1041,bq	the <term> system </term> produces the <term>	translation	output </term> sufficient for content understanding	#533 Having been trained on Korean newspaper articles on missiles and chemical biological warfare, the system produces thetranslation output sufficient for content understanding of the original document.
tech,30-1-H01-1042,bq	to the <term> output </term> of <term> machine	translation	( MT ) systems </term> . We believe that	#575 The purpose of this research is to test the efficacy of applying automated evaluation techniques, originally devised for the evaluation of human language learners, to the output of machine translation (MT) systems.
other,18-2-H01-1042,bq	language learning process </term> , the <term>	translation	process </term> and the <term> development </term>	#599 We believe that these evaluation techniques will provide information about both the human language learning process, thetranslation process and the development of machine translation systems.
tech,24-2-H01-1042,bq	the <term> development </term> of <term> machine	translation	systems </term> . This , the first experiment	#606 We believe that these evaluation techniques will provide information about both the human language learning process, the translation process and the development of machine translation systems.
other,16-6-H01-1042,bq	duplicating the experiment using <term> machine	translation	output </term> . Subjects were given a set	#679 We tested this to see if similar criteria could be elicited from duplicating the experiment using machine translation output.
other,11-8-H01-1042,bq	translations </term> , others were <term> machine	translation	outputs </term> . The subjects were given	#709 Some of the extracts were expert human translations, others were machine translation outputs.
other,19-9-H01-1042,bq	sample output to be an <term> expert human	translation	</term> or a <term> machine translation </term>	#733 The subjects were given three minutes per extract to determine whether they believed the sample output to be an expert human translation or a machine translation.
other,24-9-H01-1042,bq	human translation </term> or a <term> machine	translation	</term> . Additionally , they were asked	#737 The subjects were given three minutes per extract to determine whether they believed the sample output to be an expert human translation or a machine translation.
tech,23-1-P01-1004,bq	<term> retrieval performance </term> of a <term>	translation	memory system </term> . We take a selection	#1484 In this paper, we compare the relative effects of segment order, segmentation and segment contiguity on the retrieval performance of atranslation memory system.
tech,4-3-P01-1007,bq	complexity </term> . For example , after <term>	translation	</term> into an equivalent <term> RCG </term>	#1659 For example, aftertranslation into an equivalent RCG, any tree adjoining grammar can be parsed in O(n6) time.
model,4-1-N03-1017,bq	% ) . We propose a new <term> phrase-based	translation	model </term> and <term> decoding algorithm	#2544 We propose a new phrase-based translation model and decoding algorithm that enables us to evaluate and compare several, previously proposed phrase-based translation models.
model,21-1-N03-1017,bq	, previously proposed <term> phrase-based	translation	models </term> . Within our framework , we	#2561 We propose a new phrase-based translation model and decoding algorithm that enables us to evaluate and compare several, previously proposed phrase-based translation models.
lr,34-3-N03-1018,bq	<term> automatic extraction </term> of <term>	translation	lexicons </term> from <term> printed text </term>	#2778 We present an implementation of the model based on finite-state models, demonstrate the model's ability to significantly reduce character and word error rate, and provide evaluation results involving automatic extraction oftranslation lexicons from printed text.
other,5-1-N03-2006,bq	N-grams </term> . In order to boost the <term>	translation	quality </term> of <term> EBMT </term> based	#3084 In order to boost thetranslation quality of EBMT based on a small-sized bilingual corpus, we use an out-of-domain bilingual corpus and, in addition, the language model of an in-domain monolingual corpus.
tech,11-1-N03-2036,bq	model </term> for <term> statistical machine	translation	</term> that uses a much simpler set of <term>	#3402 In this paper, we describe a phrase-based unigram model for statistical machine translation that uses a much simpler set of model parameters than similar phrase-based models.
other,1-2-N03-2036,bq	phrase-based models </term> . The <term> units of	translation	</term> are <term> blocks </term> - pairs of <term>	#3420 The units of translation are blocks - pairs of phrases.
tech,6-2-P03-1050,bq	</term> is based on <term> statistical machine	translation	</term> and it uses an <term> English stemmer	#4454 The stemming model is based on statistical machine translation and it uses an English stemmer and a small (10K sentences) parallel corpus as its sole training resources.


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