Negative filter
Dialogue, strategies 25
(800.4 per million)
tech,3-1-P03-1033,ak
effective . We address appropriate
<term>
user modeling
</term>
in order to generate
<term>
cooperative
#4284We address appropriateuser modeling in order to generate cooperative responses to each user in spoken dialogue systems.
other,9-1-P03-1033,ak
modeling
</term>
in order to generate
<term>
cooperative responses
</term>
to each user in
<term>
spoken dialogue
#4290We address appropriate user modeling in order to generatecooperative responses to each user in spoken dialogue systems.
tech,15-1-P03-1033,ak
cooperative responses
</term>
to each user in
<term>
spoken dialogue systems
</term>
. Unlike previous studies that focus
#4296We address appropriate user modeling in order to generate cooperative responses to each user inspoken dialogue systems.
other,6-2-P03-1033,ak
Unlike previous studies that focus on
<term>
user 's knowledge
</term>
or typical kinds of
<term>
users
</term>
#4306Unlike previous studies that focus onuser 's knowledge or typical kinds of users, the user model we propose is more comprehensive.
other,13-2-P03-1033,ak
knowledge
</term>
or typical kinds of
<term>
users
</term>
, the
<term>
user model
</term>
we propose
#4313Unlike previous studies that focus on user's knowledge or typical kinds ofusers, the user model we propose is more comprehensive.
model,16-2-P03-1033,ak
typical kinds of
<term>
users
</term>
, the
<term>
user model
</term>
we propose is more comprehensive
#4316Unlike previous studies that focus on user's knowledge or typical kinds of users, theuser model we propose is more comprehensive.
other,6-3-P03-1033,ak
comprehensive . Specifically , we set up three
<term>
dimensions
</term>
of
<term>
user models
</term>
:
<term>
#4330Specifically, we set up threedimensions of user models: skill level to the system, knowledge level on the target domain and the degree of hastiness.
model,8-3-P03-1033,ak
up three
<term>
dimensions
</term>
of
<term>
user models
</term>
:
<term>
skill level
</term>
to the
<term>
#4332Specifically, we set up three dimensions ofuser models: skill level to the system, knowledge level on the target domain and the degree of hastiness.
other,11-3-P03-1033,ak
</term>
of
<term>
user models
</term>
:
<term>
skill level
</term>
to the
<term>
system
</term>
,
<term>
knowledge
#4335Specifically, we set up three dimensions of user models:skill level to the system, knowledge level on the target domain and the degree of hastiness.
tech,15-3-P03-1033,ak
</term>
:
<term>
skill level
</term>
to the
<term>
system
</term>
,
<term>
knowledge level
</term>
on the
#4339Specifically, we set up three dimensions of user models: skill level to thesystem, knowledge level on the target domain and the degree of hastiness.
other,17-3-P03-1033,ak
level
</term>
to the
<term>
system
</term>
,
<term>
knowledge level
</term>
on the
<term>
target domain
</term>
and
#4341Specifically, we set up three dimensions of user models: skill level to the system,knowledge level on the target domain and the degree of hastiness.
other,21-3-P03-1033,ak
<term>
knowledge level
</term>
on the
<term>
target domain
</term>
and the degree of
<term>
hastiness
</term>
#4345Specifically, we set up three dimensions of user models: skill level to the system, knowledge level on thetarget domain and the degree of hastiness.
other,27-3-P03-1033,ak
target domain
</term>
and the degree of
<term>
hastiness
</term>
. Moreover , the
<term>
models
</term>
#4351Specifically, we set up three dimensions of user models: skill level to the system, knowledge level on the target domain and the degree ofhastiness.
model,3-4-P03-1033,ak
<term>
hastiness
</term>
. Moreover , the
<term>
models
</term>
are automatically derived by
<term>
#4356Moreover, themodels are automatically derived by decision tree learning using real dialogue data collected by the system.
tech,8-4-P03-1033,ak
</term>
are automatically derived by
<term>
decision tree learning
</term>
using real
<term>
dialogue data
</term>
#4361Moreover, the models are automatically derived bydecision tree learning using real dialogue data collected by the system.
lr,13-4-P03-1033,ak
decision tree learning
</term>
using real
<term>
dialogue data
</term>
collected by the
<term>
system
</term>
#4366Moreover, the models are automatically derived by decision tree learning using realdialogue data collected by the system.
tech,18-4-P03-1033,ak
dialogue data
</term>
collected by the
<term>
system
</term>
. We obtained reasonable
<term>
classification
#4371Moreover, the models are automatically derived by decision tree learning using real dialogue data collected by thesystem.
measure(ment),3-5-P03-1033,ak
system
</term>
. We obtained reasonable
<term>
classification accuracy
</term>
for all
<term>
dimensions
</term>
.
<term>
#4376We obtained reasonableclassification accuracy for all dimensions.
other,7-5-P03-1033,ak
classification accuracy
</term>
for all
<term>
dimensions
</term>
.
<term>
Dialogue strategies
</term>
#4380We obtained reasonable classification accuracy for alldimensions.
tech,5-6-P03-1033,ak
Dialogue strategies
</term>
based on the
<term>
user modeling
</term>
are implemented in Kyoto city bus
#4387Dialogue strategies based on theuser modeling are implemented in Kyoto city bus information system that has been developed at our laboratory.