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 rulebased 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 handcrafted system.

• Type: Conference
• Year: 2001
• Where: ACL
• Authors: Owen Rambow, Monica Rogati, Marilyn A. Walker
• Keywords: ACL 2001, ACL 2007, Rule-based Sentence Planners, Sentence Planner, Trainable Sentence Planner