#981The issue of system response to users has been extensively studied by thenatural language generation community, though rarely in the context of dialog systems.
other,22-2-H01-1055,ak
</term>
understand more of what the
<term>
user
</term>
tells them , they need to be more
#952However, the improved speech recognition has brought to light a new problem: as dialog systems understand more of what theuser tells them, they need to be more sophisticated at responding to the user.
other,3-3-H01-1055,ak
the
<term>
user
</term>
. The issue of
<term>
system response
</term>
to
<term>
users
</term>
has been extensively
#971The issue ofsystem response to users has been extensively studied by the natural language generation community, though rarely in the context of dialog systems.
other,36-2-H01-1055,ak
sophisticated at responding to the
<term>
user
</term>
. The issue of
<term>
system response
#966However, the improved speech recognition has brought to light a new problem: as dialog systems understand more of what the user tells them, they need to be more sophisticated at responding to theuser.
other,6-3-H01-1055,ak
issue of
<term>
system response
</term>
to
<term>
users
</term>
has been extensively studied by the
#974The issue of system response tousers has been extensively studied by the natural language generation community, though rarely in the context of dialog systems.
tech,10-4-H01-1055,ak
generation
</term>
can be adapted to
<term>
dialog systems
</term>
, and how the high cost of hand-crafting
#1005We show how research in generation can be adapted todialog systems, and how the high cost of hand-crafting knowledge-based generation systems can be overcome by employing machine learning techniques.
tech,14-1-H01-1055,ak
put the goal of naturally sounding
<term>
dialog systems
</term>
within reach . However , the improved
#925Recent advances in Automatic Speech Recognition technology have put the goal of naturally soundingdialog systems within reach.
tech,15-2-H01-1055,ak
brought to light a new problem : as
<term>
dialog systems
</term>
understand more of what the
<term>
#945However, the improved speech recognition has brought to light a new problem: asdialog systems understand more of what the user tells them, they need to be more sophisticated at responding to the user.
tech,20-4-H01-1055,ak
how the high cost of hand-crafting
<term>
knowledge-based generation systems
</term>
can be overcome by employing
<term>
#1015We show how research in generation can be adapted to dialog systems, and how the high cost of hand-craftingknowledge-based generation systems can be overcome by employing machine learning techniques.
tech,24-3-H01-1055,ak
</term>
, though rarely in the context of
<term>
dialog systems
</term>
. We show how research in
<term>
generation
#992The issue of system response to users has been extensively studied by the natural language generation community, though rarely in the context ofdialog systems.
tech,28-4-H01-1055,ak
</term>
can be overcome by employing
<term>
machine learning techniques
</term>
. In this paper , we address the
#1023We show how research in generation can be adapted to dialog systems, and how the high cost of hand-crafting knowledge-based generation systems can be overcome by employingmachine learning techniques.
tech,3-1-H01-1055,ak
in new domains . Recent advances in
<term>
Automatic Speech Recognition technology
</term>
have put the goal of naturally sounding
#914Recent advances inAutomatic Speech Recognition technology have put the goal of naturally sounding dialog systems within reach.
tech,4-2-H01-1055,ak
within reach . However , the improved
<term>
speech recognition
</term>
has brought to light a new problem
#934However, the improvedspeech recognition has brought to light a new problem: as dialog systems understand more of what the user tells them, they need to be more sophisticated at responding to the user.
tech,5-4-H01-1055,ak
systems
</term>
. We show how research in
<term>
generation
</term>
can be adapted to
<term>
dialog systems
#1000We show how research ingeneration can be adapted to dialog systems, and how the high cost of hand-crafting knowledge-based generation systems can be overcome by employing machine learning techniques.