interesting information piece would be found in a <term> large database </term> . Traditional
automatic detection </term> of those activities in meeting situation and everyday rejoinders
obtained . To support engaging human users in robust , <term> mixed-initiative speech dialogue
</term> which reach beyond current capabilities in <term> dialogue systems </term> , the <term>
Communicator </term> participants are using . In this presentation , we describe the features
infrastructure </term> for this purpose . In this paper we show how two standard outputs
We describe how this information is used in a <term> prototype system </term> designed
systems </term> . This , the first experiment in a series of experiments , looks at the <term>
native from non-native language essays </term> in less than 100 <term> words </term> . Even more
preliminary analysis of the factors involved in the decision making process will be presented
complete . We have demonstrated this capability in several field exercises with the Marines
applications of this <term> technology </term> in <term> new domains </term> . Recent advances
<term> new domains </term> . Recent advances in <term> Automatic Speech Recognition technology
generation community </term> , though rarely in the context of <term> dialog systems </term>
dialog systems </term> . We show how research in <term> generation </term> can be adapted to
<term> machine learning techniques </term> . In this paper , we address the problem of
</term> . We describe our use of this approach in numerous fielded <term> user studies </term>
reported more than 99 % <term> accuracy </term> in both <term> language identification </term>
into one or more <term> sentences </term> . In this paper , we present <term> SPoT </term>
<term> top human-ranked sentence plan </term> . In this paper , we compare the relative effects
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