| tech,7-1-P01-1056,ak | Techniques for automatically training 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 trainable components can compete with <term> hand-crafted template-based or rule-based approaches </term> . | #2020 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,ak | Techniques for automatically training 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 trainable components can compete with <term> hand-crafted template-based or rule-based approaches </term> . | #2035 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,ak | Techniques for automatically training 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 trainable components can compete with <term> hand-crafted template-based or rule-based approaches </term> . | #2037 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. | |
| tech,32-1-P01-1056,ak | Techniques for automatically training 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 trainable components can compete with <term> hand-crafted template-based or rule-based approaches </term> . | #2045 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,ak | 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> . | #2058 In this paper We experimentally evaluate atrainable sentence planner for a spoken dialogue system by eliciting subjective human judgments. | |
| tech,12-2-P01-1056,ak | 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> . | #2063 In this paper We experimentally evaluate a trainable sentence planner for aspoken dialogue system by eliciting subjective human judgments. | |
| other,17-2-P01-1056,ak | 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> . | #2068 In this paper We experimentally evaluate a trainable sentence planner for a spoken dialogue system by elicitingsubjective human judgments. | |
| tech,12-3-P01-1056,ak | 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> . | #2084 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,ak | 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> . | #2090 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,ak | 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> . | #2096 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,ak | 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> . | #2104 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,ak | 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> . | #2111 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,ak | 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> . | #2115 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,ak | 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> . | #2122 We show that the trainable sentence planner performs better than the rule-based systems and the baselines, and as well as thehand-crafted system. |
