  Large-scale natural language generation requires the integration of vast
amounts of knowledge: lexical, grammatical, and conceptual. A robust generator
must be able to operate well even when pieces of knowledge are missing. It must
also be robust against incomplete or inaccurate inputs. To attack these
problems, we have built a hybrid generator, in which gaps in symbolic knowledge
are filled by statistical methods. We describe algorithms and show experimental
results. We also discuss how the hybrid generation model can be used to
simplify current generators and enhance their portability, even when perfect
knowledge is in principle obtainable.
