Paper: Evaluating A Trainable Sentence Planner For A Spoken Dialogue System

ACL ID P01-1056
Title Evaluating A Trainable Sentence Planner For A Spoken Dialogue System
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2001
Authors

Techniques for automatically training modules of a natural language gener- ator 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 ex- perimentally evaluate a trainable sen- tence planner for a spoken dialogue sys- tem by eliciting subjective human judg- ments. In order to perform an ex- haustive comparison, we also evaluate a hand-crafted template-based genera- tion component, two rule-based sen- tence planners, and two baseline sen- tence planners. We show that the train- able sentence planner performs better than the rule-based systems and the baselines, and as well as the hand- crafted system.