Paper: Balancing Data-Driven And Rule-Based Approaches In The Context Of A Multimodal Conversational System

ACL ID N04-1005
Title Balancing Data-Driven And Rule-Based Approaches In The Context Of A Multimodal Conversational System
Venue Human Language Technologies
Session Main Conference
Year 2004
Authors

Moderate-sized rule-based spoken language models for recognition and understanding are easy to develop and provide the ability to rapidly prototype conversational applications. However, scalability of such systems is a bot- tleneck due to the heavy cost of authoring and maintenance of rule sets and inevitable brittle- ness due to lack of coverage in the rule sets. In contrast, data-driven approaches are robust and the procedure for model building is usu- ally simple. However, the lack of data in a par- ticular application domain limits the ability to build data-driven models. In this paper, we ad- dress the issue of combining data-driven and grammar-based models for rapid prototyping of robust speech recognition and understanding models for a multimodal conversational sys- tem. We also pre...