Paper: Two-Level Many-Paths Generation

ACL ID P95-1034
Title Two-Level Many-Paths Generation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1995

Large-scale natural language generation re- quires the integration of vast mounts of knowledge: lexical, grammatical, and concep- tual. A robust generator must be able to operate well even when pieces of knowledge axe missing. It must also be robust against incomplete or inaccurate inputs. To attack these problems, we have built a hybrid gen- erator, 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.