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other,0-2-A94-1017,bq	translation </term> . <term> Spoken language	translation	</term> requires ( 1 ) an accurate <term> translation	#20212 Spoken language translation requires (1) an accurate translation and (2) a real-time response.
other,11-3-I05-6011,bq	pronouns </term> and improving <term> machine	translation	outputs </term> . Annotating <term> honorifics	#8613 This referential information is vital for resolving zero pronouns and improving machine translation outputs.
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-P05-1074,bq	from <term> phrase-based statistical machine	translation	</term> , we show how <term> paraphrases </term>	#9694 Using alignment techniques from phrase-based statistical machine translation, we show how paraphrases in one language can be identified using a phrase in another language as a pivot.
measure(ment),16-2-N04-1022,bq	<term> loss functions </term> that measure <term>	translation	performance </term> . We describe a hierarchy	#6572 This statistical approach aims to minimize expected loss of translation errors under loss functions that measuretranslation performance.
tech,8-1-H05-1117,bq	automatic evaluation </term> of <term> machine	translation	</term> and <term> document summarization </term>	#7514 Following recent developments in the automatic evaluation of machine translation and document summarization, we present a similar approach, implemented in a measure called POURPRE, for automatically evaluating answers to definition questions.
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.
lr,9-1-H05-2007,bq	systematic <term> patterns </term> in <term>	translation	data </term> using <term> part-of-speech tag	#7637 We describe a method for identifying systematic patterns intranslation data using part-of-speech tag sequences.
other,16-1-H05-1005,bq	</term> to correct errors in <term> machine	translation	</term> and thus improve the quality of <term>	#7142 In this paper, we use the information redundancy in multilingual input to correct errors in machine translation and thus improve the quality of multilingual summaries.
measure(ment),20-1-I05-5008,bq	reference sets </term> in objective <term> machine	translation	evaluation measures </term> like <term> BLEU	#8462 We propose a method that automatically generates paraphrase sets from seed sentences to be used as reference sets in objective machine translation evaluation measures like BLEU and NIST.
tech,17-1-P05-1069,bq	model </term> for <term> statistical machine	translation	( SMT ) </term> . The <term> model </term> predicts	#9568 In this paper, we present a novel training method for a localized phrase-based prediction model for statistical machine translation (SMT).
tech,13-3-I05-2021,bq	scores </term> of <term> statistical machine	translation	( SMT ) </term> suggests that <term> SMT models	#7871 At the same time, the recent improvements in the BLEU scores of statistical machine translation (SMT) suggests that SMT models are good at predicting the right translation of the words in source language sentences.
tech,10-3-C88-2160,bq	limited to the analysis step of the <term>	translation	process </term> . This paper presents a new	#15706 The interaction is limited to the analysis step of thetranslation process.
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.
tech,12-1-P06-4014,bq	NLP components </term> into a <term> machine	translation	pipeline </term> that capitalizes on <term>	#11802 The LOGON MT demonstrator assembles independently valuable general-purpose NLP components into a machine translation pipeline that capitalizes on output quality.
tech,6-1-P05-1034,bq	approach </term> to <term> statistical machine	translation	</term> that combines <term> syntactic information	#9209 We describe a novel approach to statistical machine translation that combines syntactic information in the source language with recent advances in phrasal translation.
tech,16-1-P05-1048,bq	models </term> help <term> statistical machine	translation	</term><term> quality </term> ? We present	#9329 We directly investigate a subject of much recent debate: do word sense disambigation models help statistical machine translation quality?
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.
measure(ment),23-3-H05-1095,bq	</term> based on the maximization of <term>	translation	accuracy </term> , as measured with the <term>	#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.
other,29-3-I05-2021,bq	</term> are good at predicting the right <term>	translation	</term> of the <term> words </term> in <term> source	#7885 At the same time, the recent improvements in the BLEU scores of statistical machine translation (SMT) suggests that SMT models are good at predicting the righttranslation of the words in source language sentences.


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