browsers </term> . At MIT Lincoln Laboratory , we have been developing a <term> Korean-to-English
</term> and <term> information sources </term> . We have built and will demonstrate an application
Automatic Speech Recognition technology </term> have put the goal of naturally sounding <term>
a <term> natural language generator </term> have recently been proposed , but a fundamental
word-level alignment models </term> does not have a strong impact on performance . Learning
for <term> language understanding </term> and have a high accuracy but little robustness and
understanding process </term> . Experiment results have shown that a <term> system </term> that exploits
that <term> manually sense-tagged data </term> have in their <term> sense coverage </term> . Our
formulate our <term> heuristic principles </term> have significant <term> predictive power </term>
</term> , noting that published results to date have not been comparable across <term> corpora
probabilistic translation models </term> that have recently been adopted in the literature
contrary , current <term> SMT models </term> do have limitations in comparison with dedicated
the last few years dramatic improvements have been made , and a number of comparative
Machine Translation ( SMT ) </term> but which have not been addressed satisfactorily by the
two ways . We first apply approaches that have been proposed for <term> predicting top-level
placing <term> commas </term> . Finally , we have shown that these results can be improved
English , many systems to run off texts have been developed . In this paper , we report
<term> natural language interfaces </term> have concentrated largely on determining the
bottom-up pattern-matching parser </term> that we have designed and implemented to provide these
large <term> text-understanding systems </term> have been designed under the assumption that
hide detail