translation ( MT ) systems </term> . We believe that these <term> evaluation techniques </term>
language learning experiment </term> showed that <term> assessors </term> can differentiate <term>
with <term> intelligent mobile agents </term> that mediate between <term> users </term> and <term>
<term> language models ( LMs ) </term> . We find that simple <term> interpolation methods </term>
</term> . We provide experimental results that clearly show the need for a <term> dynamic
performance </term> further . We suggest a method that mimics the behavior of the <term> oracle </term>
from <term> training data </term> . We show that the trained <term> SPR </term> learns to select
two distinct <term> datasets </term> , we find that <term> indexing </term> according to simple
but much faster . We also provide evidence that our findings are scalable . The theoretical
<term> queries </term> containing them . I show that the <term> performance </term> of a <term> search
approximation of the <term> formal analysis </term> that is compatible with the <term> search engine
semantics </term> . The value of this approach is that as the <term> operational semantics </term>
</term> of <term> Minimalist grammars </term> , that are <term> Stabler 's formalization </term>
baseline sentence planners </term> . We show that the <term> trainable sentence planner </term>
for <term> utterance classification </term> that does not require <term> manual transcription
utterance classification performance </term> that is surprisingly close to what can be achieved
multi-level answer resolution algorithm </term> that combines results from the <term> answering
<term> annotation experiment </term> and showed that <term> human annotators </term> can reliably
against the <term> annotated data </term> shows that , it successfully classifies 73.2 % in
</term> and <term> decoding algorithm </term> that enables us to evaluate and compare several
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