<term> speech recognition </term> has brought to light a new problem : as <term> dialog systems
the <term> user </term> tells them , they need to be more sophisticated at responding to
need to be more sophisticated at responding to the <term> user </term> . The issue of <term>
The issue of <term> system response </term> to <term> users </term> has been extensively studied
in <term> generation </term> can be adapted to <term> dialog systems </term> , and how the
dynamic language model combination </term> to improve the <term> performance </term> further
decision tree </term> . The method amounts to tagging <term> LMs </term> with <term> confidence
best <term> hypothesis </term> corresponding to the <term> LM </term> with the best <term> confidence
</term> applying <term> mutual information </term> to reduce the <term> error-correction rules </term>
speech acts </term> and the decision of how to combine them into one or more <term> sentences
show that the trained <term> SPR </term> learns to select a <term> sentence plan </term> whose
find that <term> indexing </term> according to simple <term> character bigrams </term> produces
a <term> retrieval accuracy </term> superior to any of the tested <term> word N-gram models
<term> bag-of-words methods </term> are shown to be equivalent to <term> segment order-sensitive
methods </term> are shown to be equivalent to <term> segment order-sensitive methods </term>
parsing technique </term> whose purpose is to improve the practical efficiency of <term>
systems use manual or semi-automatic methods to collect <term> paraphrases </term> . We present
frequently enough in <term> dialog </term> to warrant serious <term> attention </term> ,
Our <term> logical definition </term> leads to a neat relation to <term> categorial grammar
definition </term> leads to a neat relation to <term> categorial grammar </term> , ( yielding
hide detail