tech,10-1-H01-1041,bq developing a <term> Korean-to-English machine translation system </term><term> CCLINC ( Common Coalition
tech,30-1-H01-1042,bq to the <term> output </term> of <term> machine translation ( MT ) systems </term> . We believe that
tech,23-1-P01-1004,bq <term> retrieval performance </term> of a <term> translation memory system </term> . We take a selection
tech,4-3-P01-1007,bq complexity </term> . For example , after <term> translation </term> into an equivalent <term> RCG </term>
lr,12-2-P01-1008,bq </term> from a <term> corpus of multiple English translations </term> of the same <term> source text </term>
model,4-1-N03-1017,bq % ) . We propose a new <term> phrase-based translation model </term> and <term> decoding algorithm
lr,34-3-N03-1018,bq <term> automatic extraction </term> of <term> translation lexicons </term> from <term> printed text </term>
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>
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,16-1-H05-1005,bq </term> to correct errors in <term> machine translation </term> and thus improve the quality of <term>
tech,13-2-H05-1012,bq material </term> for problems in <term> machine translation </term> and that a mixture of <term> supervised
tech,4-1-H05-1095,bq a <term> phrase-based statistical machine translation method </term> , based on <term> non-contiguous
model,8-1-H05-1101,bq </term> associated with <term> probabilistic translation models </term> that have recently been adopted
tech,8-1-H05-1117,bq automatic evaluation </term> of <term> machine translation </term> and <term> document summarization </term>
lr,9-1-H05-2007,bq systematic <term> patterns </term> in <term> translation data </term> using <term> part-of-speech tag
tech,4-1-I05-2014,bq evaluation metrics </term> for <term> Machine Translation ( MT ) systems </term> , such as <term> BLEU
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