A Semantic Head‐driven Generation System for Feature Structure Tree‐adjoining Grammars

Abstract

This paper describes a natural language generation system developed for a spoken language translation system. Our system employs Feature-structure-based Tree Adjoining Grammar (FTAG) for generation knowledge representation. Each elementary tree of our grammar is paired with a semantic feature structure which is consistent with semantics defined in Head-driven Phrase Structure Grammar. Feature structures attached to nodes of elementary trees are unrestricted. Thus our formalism allows HPSG style phrase structure description as well as TAG style description. The advantage of our generation knowledge representation is the ability of incorporating HPSG style "core" grammar and TAG style case-based grammar.The system generates a syntactic tree by combining elementary trees so as to satisfy an input semantic structure. The generation algorithm is an application of a semantic head-driven generation. To cany out an adjoining operation, an elementary tree with an adjunction node is dynamically split at the adjunction node during generation.