Evaluating a Trainable Sentence Planner for a Spoken Dialogue System 
 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 . In this paper We experimentally evaluate a trainable sentence planner for a spoken dialogue system by eliciting subjective human judgments . In order to perform an exhaustive comparison, we also evaluate a hand-crafted template-based generation component , two rule-based sentence planners , and two baseline sentence planners . We show that the trainable sentence planner performs better than the rule-based systems and the baselines , and as well as the hand-crafted system . 