#2020Techniques for automatically training modules of anatural language generator have recently been proposed, but a fundamental concern is whether the quality of utterances produced with trainable components can compete with hand-crafted template-based or rule-based approaches.
measure(ment),22-1-P01-1056,ak
fundamental concern is whether the
<term>
quality
</term>
of
<term>
utterances
</term>
produced
#2035Techniques for automatically training modules of a natural language generator have recently been proposed, but a fundamental concern is whether thequality of utterances produced with trainable components can compete with hand-crafted template-based or rule-based approaches.
other,24-1-P01-1056,ak
whether the
<term>
quality
</term>
of
<term>
utterances
</term>
produced with trainable components
#2037Techniques for automatically training modules of a natural language generator have recently been proposed, but a fundamental concern is whether the quality ofutterances produced with trainable components can compete with hand-crafted template-based or rule-based approaches.
tech,32-1-P01-1056,ak
trainable components can compete with
<term>
hand-crafted template-based or rule-based approaches
</term>
. In this paper We experimentally
#2045Techniques for automatically training modules of a natural language generator have recently been proposed, but a fundamental concern is whether the quality of utterances produced with trainable components can compete withhand-crafted template-based or rule-based approaches.
tech,7-2-P01-1056,ak
paper We experimentally evaluate a
<term>
trainable sentence planner
</term>
for a
<term>
spoken dialogue system
#2058In this paper We experimentally evaluate atrainable sentence planner for a spoken dialogue system by eliciting subjective human judgments.
tech,12-2-P01-1056,ak
trainable sentence planner
</term>
for a
<term>
spoken dialogue system
</term>
by eliciting
<term>
subjective human
#2063In this paper We experimentally evaluate a trainable sentence planner for aspoken dialogue system by eliciting subjective human judgments.
other,17-2-P01-1056,ak
dialogue system
</term>
by eliciting
<term>
subjective human judgments
</term>
. In order to perform an exhaustive
#2068In this paper We experimentally evaluate a trainable sentence planner for a spoken dialogue system by elicitingsubjective human judgments.
tech,12-3-P01-1056,ak
exhaustive comparison , we also evaluate a
<term>
hand-crafted template-based generation component
</term>
, two
<term>
rule-based sentence planners
#2084In order to perform an exhaustive comparison, we also evaluate ahand-crafted template-based generation component, two rule-based sentence planners, and two baseline sentence planners.
tech,18-3-P01-1056,ak
template-based generation component
</term>
, two
<term>
rule-based sentence planners
</term>
, and two
<term>
baseline sentence
#2090In order to perform an exhaustive comparison, we also evaluate a hand-crafted template-based generation component, tworule-based sentence planners, and two baseline sentence planners.
tech,24-3-P01-1056,ak
sentence planners
</term>
, and two
<term>
baseline sentence planners
</term>
. We show that the
<term>
trainable
#2096In order to perform an exhaustive comparison, we also evaluate a hand-crafted template-based generation component, two rule-based sentence planners, and twobaseline sentence planners.
tech,4-4-P01-1056,ak
planners
</term>
. We show that the
<term>
trainable sentence planner
</term>
performs better than the
<term>
rule-based
#2104We show that thetrainable sentence planner performs better than the rule-based systems and the baselines, and as well as the hand-crafted system.
tech,11-4-P01-1056,ak
planner
</term>
performs better than the
<term>
rule-based systems
</term>
and the
<term>
baselines
</term>
, and
#2111We show that the trainable sentence planner performs better than therule-based systems and the baselines, and as well as the hand-crafted system.
tech,15-4-P01-1056,ak
<term>
rule-based systems
</term>
and the
<term>
baselines
</term>
, and as well as the
<term>
hand-crafted
#2115We show that the trainable sentence planner performs better than the rule-based systems and thebaselines, and as well as the hand-crafted system.