measure(ment),8-3-H01-1040,bq |
results of a preliminary ,
<term>
qualitative
|
user
|
evaluation
</term>
of the
<term>
system
</term>
|
#353
We also report results of a preliminary, qualitative user evaluation of the system, which while broadly positive indicates further work needs to be done on the interface to make users aware of the increased potential of IE-enhanced text browsers. |
other,22-2-H01-1055,bq |
</term>
understand more of what the
<term>
|
user
|
</term>
tells them , they need to be more
|
#952
However, 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,36-2-H01-1055,bq |
sophisticated at responding to the
<term>
|
user
|
</term>
. The issue of
<term>
system response
|
#966
However, 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. |
measure(ment),4-2-H01-1068,bq |
systems
</term>
. The three tiers measure
<term>
|
user
|
satisfaction
</term>
,
<term>
system support
|
#1211
The three tiers measureuser satisfaction, system support of mission success and component performance. |
other,10-3-H01-1068,bq |
this approach in numerous fielded
<term>
|
user
|
studies
</term>
conducted with the U.S. military
|
#1233
We describe our use of this approach in numerous fieldeduser studies conducted with the U.S. military. |
other,22-2-P01-1070,bq |
target variables which represent a
<term>
|
user
|
's informational goals
</term>
. We report
|
#2165
These models, which are built from shallow linguistic features of questions, are employed to predict target variables which represent auser's informational goals. |
other,7-2-P03-1031,bq |
the
<term>
system
</term>
to understand
<term>
|
user
|
utterances
</term>
based on the
<term>
context
|
#4145
This process enables the system to understanduser utterances based on the context of a dialogue. |
other,12-3-P03-1031,bq |
</term>
result can be obtained for a
<term>
|
user
|
utterance
</term>
due to the
<term>
ambiguity
|
#4167
Since multiple candidates for the understanding result can be obtained for auser utterance due to the ambiguity of speech understanding, it is not appropriate to decide on a single understandingresult after each user utterance. |
other,34-3-P03-1031,bq |
understandingresult
</term>
after each
<term>
|
user
|
utterance
</term>
. By holding multiple
<term>
|
#4189
Since multiple candidates for the understanding result can be obtained for a user utterance due to the ambiguity of speech understanding, it is not appropriate to decide on a single understandingresult after eachuser utterance. |
tech,3-1-P03-1033,bq |
effective . We address appropriate
<term>
|
user
|
modeling
</term>
in order to generate
<term>
|
#4282
We address appropriateuser modeling in order to generate cooperative responses to each user in spoken dialogue systems. |
other,13-1-P03-1033,bq |
cooperative responses
</term>
to each
<term>
|
user
|
</term>
in
<term>
spoken dialogue systems
</term>
|
#4292
We address appropriate user modeling in order to generate cooperative responses to eachuser in spoken dialogue systems. |
other,6-2-P03-1033,bq |
Unlike previous studies that focus on
<term>
|
user
|
</term>
's
<term>
knowledge
</term>
or typical
|
#4304
Unlike previous studies that focus onuser's knowledge or typical kinds of users, the user model we propose is more comprehensive. |
model,16-2-P03-1033,bq |
typical kinds of
<term>
users
</term>
, the
<term>
|
user
|
model
</term>
we propose is more comprehensive
|
#4314
Unlike previous studies that focus on user's knowledge or typical kinds of users, theuser model we propose is more comprehensive. |
model,8-3-P03-1033,bq |
Specifically , we set up three dimensions of
<term>
|
user
|
models
</term>
:
<term>
skill level
</term>
to
|
#4330
Specifically, we set up three dimensions ofuser models: skill level to the system, knowledge level on the target domain and the degree of hastiness. |
tech,5-6-P03-1033,bq |
Dialogue strategies
</term>
based on the
<term>
|
user
|
modeling
</term>
are implemented in
<term>
|
#4385
Dialogue strategies based on theuser modeling are implemented in Kyoto city bus information system that has been developed at our laboratory. |
other,4-2-P05-3025,bq |
</term>
. The
<term>
method
</term>
allows a
<term>
|
user
|
</term>
to explore a
<term>
model
</term>
of
<term>
|
#9852
The method allows auser to explore a model of syntax-based statistical machine translation (MT), to understand the model's strengths and weaknesses, and to compare it to other MT systems. |
other,28-2-P06-4007,bq |
users
</term>
need by analyzing how a
<term>
|
user
|
</term>
interacts with a system while gathering
|
#11682
FERRET utilizes a novel approach to Q/A known as predictive questioning which attempts to identify the questions (and answers) that users need by analyzing how auser interacts with a system while gathering information related to a particular scenario. |
tech,6-2-J88-3002,bq |
</term>
. This paper explores the role of
<term>
|
user
|
modeling
</term>
in such
<term>
systems
</term>
|
#16043
This paper explores the role ofuser modeling in such systems. |
model,8-3-J88-3002,bq |
with a characterization of what a
<term>
|
user
|
model
</term>
is and how it can be used .
|
#16057
It begins with a characterization of what auser model is and how it can be used. |
model,6-4-J88-3002,bq |
. The types of information that a
<term>
|
user
|
model
</term>
may be required to keep about
|
#16073
The types of information that auser model may be required to keep about a user are then identified and discussed. |