Translation ( SMT ) </term> but which have not been addressed satisfactorily by the <term> SMT
translation models </term> that have recently been adopted in the literature on <term> machine
approach . <term> Word Identification </term> has been an important and active issue in <term> Chinese
variety of <term> SMT algorithms </term> have been built and empirically tested whereas little
that published results to date have not been comparable across <term> corpora </term> or
machine translation ( MT ) systems </term> has been considered to be more complicated than <term>
</term> . This data collection effort has been co-ordinated by <term> MADCOW ( Multi-site
system </term> . It has also successfully been coupled with <term> rule-based and example
<term> Chat-80 </term> . <term> Chat-80 </term> has been designed to be both efficient and easily
<term> text-understanding systems </term> have been designed under the assumption that the
English , many systems to run off texts have been developed . In this paper , we report a
city bus information system </term> that has been developed at our laboratory . Experimental
translation system </term> . <term> STTK </term> has been developed by the presenter and co-workers
</term> ) . Further , a special method has been developed for easy <term> word classification
<term> closed semantic domains </term> , have been developed in order to generate <term> lexical
</term> . At MIT Lincoln Laboratory , we have been developing a <term> Korean-to-English machine
of a <term> natural language </term> , it has been difficult to detect <term> error characters
<term> utterance </term> , but they have often been disregarded , perhaps because it seemed
</term> of <term> SMT models </term> has never been evaluated and compared with that of the
gracefully . Each of these techniques have been evaluated and the results of the evaluations
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