Instance-based natural language generation

Abstract

This paper presents a bottom-up generator that makes use of Information Retrieval techniques to rank potential generation candidates by comparing them to a data base of stored instances. We introduce two general techniques to address the search problem, expectation-driven search and dynamic grammar rule selection, and present the architecture of an implemented generation system called IGEN. Our approach uses a domain-specific generation grammar that is automatically derived from a semantically tagged treebank. We then evaluate the efficiency of our system.