via a standard <term> text browser </term> . We describe how this information is used in
their <term> industry watch </term> function . We also report results of a preliminary , <term>
machine translation ( MT ) systems </term> . We believe that these <term> evaluation techniques
<term> assessors </term> made their decisions . We tested this to see if similar criteria
human interaction with data sources </term> . We integrate a <term> spoken language understanding
</term> and <term> information sources </term> . We have built and will demonstrate an application
when a <term> request </term> is complete . We have demonstrated this capability in several
the context of <term> dialog systems </term> . We show how research in <term> generation </term>
several <term> language models ( LMs ) </term> . We find that simple <term> interpolation methods
decisions </term> using the <term> reference </term> . We provide experimental results that clearly
improve the <term> performance </term> further . We suggest a method that mimics the behavior
</term> with the best <term> confidence </term> . We describe a three-tiered approach for <term>
</term> and <term> component performance </term> . We describe our use of this approach in numerous
</term> provided by <term> human judges </term> . We reconceptualize the task into two distinct
learned from <term> training data </term> . We show that the trained <term> SPR </term> learns
a <term> translation memory system </term> . We take a selection of both <term> bag-of-words
retrieval accuracy </term> , but much faster . We also provide evidence that our findings
methods to collect <term> paraphrases </term> . We present an <term> unsupervised learning algorithm
even larger improvements are possible . We provide a <term> logical definition </term>
rule-based approaches </term> . In this paper We experimentally evaluate a <term> trainable
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