other,5-1-N03-2006,bq N-grams </term> . In order to boost the <term> translation quality </term> of <term> EBMT </term> based
tech,11-1-N03-2036,bq model </term> for <term> statistical machine translation </term> that uses a much simpler set of <term>
other,1-2-N03-2036,bq phrase-based models </term> . The <term> units of translation </term> are <term> blocks </term> - pairs of <term>
tech,6-2-P03-1050,bq </term> is based on <term> statistical machine translation </term> and it uses an <term> English stemmer
other,26-5-C04-1106,bq </term> , we relied on the idea that <term> translation </term> should preserve <term> meaning </term>
tech,13-2-C04-1192,bq </term> based on <term> automatic extraction of translation equivalents </term> and being supported by
tech,9-1-N04-1022,bq decoding </term> for <term> statistical machine translation </term> . This statistical approach aims
other,6-2-N04-1022,bq approach aims to minimize <term> expected loss of translation errors </term> under <term> loss functions </term>
measure(ment),16-2-N04-1022,bq <term> loss functions </term> that measure <term> translation performance </term> . We describe a hierarchy
other,10-4-N04-1022,bq decoders </term> on a <term> Chinese-to-English translation task </term> . Our results show that <term>
other,16-1-H05-1005,bq </term> to correct errors in <term> machine translation </term> and thus improve the quality of <term>
tech,6-4-H05-1005,bq Further , the use of multiple <term> machine translation systems </term> provides yet more <term> redundancy
other,6-5-H05-1005,bq demonstrate how errors in the <term> machine translations </term> of the input <term> Arabic documents
tech,13-2-H05-1012,bq material </term> for problems in <term> machine translation </term> and that a mixture of <term> supervised
measure(ment),15-4-H05-1012,bq well as improvement on several <term> machine translation tests </term> . <term> Performance </term> of
tech,4-1-H05-1095,bq a <term> phrase-based statistical machine translation method </term> , based on <term> non-contiguous
tech,1-3-H05-1095,bq corpora </term> is proposed . A <term> statistical translation model </term> is also presented that deals
measure(ment),23-3-H05-1095,bq </term> based on the maximization of <term> translation accuracy </term> , as measured with the <term>
other,0-4-H05-1095,bq <term> NIST evaluation metric </term> . <term> Translations </term> are produced by means of a <term> beam-search
model,8-1-H05-1101,bq </term> associated with <term> probabilistic translation models </term> that have recently been adopted
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