Concordance

Query P01-1056 16 >
Negative filter trainable, sentence, planner 14 (652.7 per million)

tech,0-1-P01-1056,bq	</term> of the <term> logical form </term> . <term>	Techniques for automatically training	</term> modules of a <term> natural language	#2012 Here we emphasize the connection to Montague semantics which can be viewed as a formal computation of the logical form.Techniques 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 with hand-crafted template-based or rule-based approaches.
tech,7-1-P01-1056,bq	automatically training </term> modules of a <term>	natural language generator	</term> have recently been proposed , but	#2019 Techniques 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,bq	fundamental concern is whether the <term>	quality	</term> of <term> utterances </term> produced	#2034 Techniques 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,bq	whether the <term> quality </term> of <term>	utterances	</term> produced with <term> trainable components	#2036 Techniques 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.
other,27-1-P01-1056,bq	<term> utterances </term> produced with <term>	trainable components	</term> can compete with <term> hand-crafted	#2039 Techniques for automatically training modules of a natural language generator have recently been proposed, but a fundamental concern is whether the quality of utterances produced withtrainable components can compete with hand-crafted template-based or rule-based approaches.
tech,32-1-P01-1056,bq	components </term> can compete with <term>	hand-crafted template-based or rule-based approaches	</term> . In this paper We experimentally	#2044 Techniques 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,12-2-P01-1056,bq	trainable sentence planner </term> for a <term>	spoken dialogue system	</term> by eliciting <term> subjective human	#2062 In this paper We experimentally evaluate a trainable sentence planner for aspoken dialogue system by eliciting subjective human judgments.
other,17-2-P01-1056,bq	dialogue system </term> by eliciting <term>	subjective human judgments	</term> . In order to perform an exhaustive	#2067 In this paper We experimentally evaluate a trainable sentence planner for a spoken dialogue system by elicitingsubjective human judgments.
tech,12-3-P01-1056,bq	exhaustive comparison , we also evaluate a <term>	hand-crafted template-based generation component	</term> , two <term> rule-based sentence planners	#2083 In 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,bq	template-based generation component </term> , two <term>	rule-based sentence planners	</term> , and two <term> baseline sentence	#2089 In 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,bq	sentence planners </term> , and two <term>	baseline sentence planners	</term> . We show that the <term> trainable	#2095 In 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,11-4-P01-1056,bq	planner </term> performs better than the <term>	rule-based systems	</term> and the <term> baselines </term> , and	#2110 We 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,bq	<term> rule-based systems </term> and the <term>	baselines	</term> , and as well as the <term> hand-crafted	#2114 We show that the trainable sentence planner performs better than the rule-based systems and thebaselines, and as well as the hand-crafted system.
tech,22-4-P01-1056,bq	baselines </term> , and as well as the <term>	hand-crafted system	</term> . We describe a set of <term> supervised	#2121 We show that the trainable sentence planner performs better than the rule-based systems and the baselines, and as well as thehand-crafted system.


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