Concordance

Query P01-1056 16 (746.0 per million)

tech,0-1-P01-1056,bq	Here we emphasize the connection to <term> Montague semantics </term> which can be viewed as a <term> formal computation </term> of the <term> logical form </term> . <term> Techniques for automatically training </term> modules of a <term> natural language generator </term> have recently been proposed , but a fundamental concern is whether the <term> quality </term> of <term> utterances </term> produced with <term> trainable components </term> can compete with <term> hand-crafted template-based or rule-based approaches </term> .	#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	<term> Techniques for automatically training </term> modules of a <term> natural language generator </term> have recently been proposed , but a fundamental concern is whether the <term> quality </term> of <term> utterances </term> produced with <term> trainable components </term> can compete with <term> hand-crafted template-based or rule-based approaches </term> .	#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	<term> Techniques for automatically training </term> modules of a <term> natural language generator </term> have recently been proposed , but a fundamental concern is whether the <term> quality </term> of <term> utterances </term> produced with <term> trainable components </term> can compete with <term> hand-crafted template-based or rule-based approaches </term> .	#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	<term> Techniques for automatically training </term> modules of a <term> natural language generator </term> have recently been proposed , but a fundamental concern is whether the <term> quality </term> of <term> utterances </term> produced with <term> trainable components </term> can compete with <term> hand-crafted template-based or rule-based approaches </term> .	#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> Techniques for automatically training </term> modules of a <term> natural language generator </term> have recently been proposed , but a fundamental concern is whether the <term> quality </term> of <term> utterances </term> produced with <term> trainable components </term> can compete with <term> hand-crafted template-based or rule-based approaches </term> .	#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	<term> Techniques for automatically training </term> modules of a <term> natural language generator </term> have recently been proposed , but a fundamental concern is whether the <term> quality </term> of <term> utterances </term> produced with <term> trainable components </term> can compete with <term> hand-crafted template-based or rule-based approaches </term> .	#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,7-2-P01-1056,bq	In this paper We experimentally evaluate a <term> trainable sentence planner </term> for a <term> spoken dialogue system </term> by eliciting <term> subjective human judgments </term> .	#2057 In this paper We experimentally evaluate atrainable sentence planner for a spoken dialogue system by eliciting subjective human judgments.
tech,12-2-P01-1056,bq	In this paper We experimentally evaluate a <term> trainable sentence planner </term> for a <term> spoken dialogue system </term> by eliciting <term> subjective human judgments </term> .	#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	In this paper We experimentally evaluate a <term> trainable sentence planner </term> for a <term> spoken dialogue system </term> by eliciting <term> subjective human judgments </term> .	#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	In order to perform an exhaustive comparison , we also evaluate a <term> hand-crafted template-based generation component </term> , two <term> rule-based sentence planners </term> , and two <term> baseline sentence planners </term> .	#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	In order to perform an exhaustive comparison , we also evaluate a <term> hand-crafted template-based generation component </term> , two <term> rule-based sentence planners </term> , and two <term> baseline sentence planners </term> .	#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	In order to perform an exhaustive comparison , we also evaluate a <term> hand-crafted template-based generation component </term> , two <term> rule-based sentence planners </term> , and two <term> baseline sentence planners </term> .	#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,4-4-P01-1056,bq	We show that the <term> trainable sentence planner </term> performs better than the <term> rule-based systems </term> and the <term> baselines </term> , and as well as the <term> hand-crafted system </term> .	#2103 We 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,bq	We show that the <term> trainable sentence planner </term> performs better than the <term> rule-based systems </term> and the <term> baselines </term> , and as well as the <term> hand-crafted system </term> .	#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	We show that the <term> trainable sentence planner </term> performs better than the <term> rule-based systems </term> and the <term> baselines </term> , and as well as the <term> hand-crafted system </term> .	#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	We show that the <term> trainable sentence planner </term> performs better than the <term> rule-based systems </term> and the <term> baselines </term> , and as well as the <term> hand-crafted system </term> .	#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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