Systems that interact with the user via natural language are in their infancy. As these systems mature and become more complex, it would be desirable for a system developer if there were an automatic method for creating natural language generation components that can produce quality output efficiently. We conduct experiments that show that this goal appears to be realizable. In particular we discuss a natural language generation system that is composed of SPoT, a trainable sentence planner, and FER-GUS, a stochastic surface realizer. We show how these stochastic NLG components can be made to work together, that they can be ported to new domains with apparent ease, and that such NLG components can be integrated in a real-time dialog system.
The system in REF comprises a trainable sentence planner and a probabilistic surface realiser.
Towards Automatic Generation Of Natural Language Generation Systems
International Conference On Computational Linguistics
Computer Science