Natural Language Generation with Abstract Machine
                Amalia -- System Demonstration

     Evgeniy Gabrilovich, Nissim Francez and Shuly Wintner


We present a system for Natural Language Generation based on an 
Abstract Machine approach. Our abstract machine operates on 
grammars encoded in a unification-based Typed Feature Structure 
formalism, and is capable of both generation and parsing. For 
efficient generation, grammars are first inverted to a suitable 
form, and then compiled into abstract machine instructions. 
A dual compiler translates the same input grammar into an abstract 
machine program for parsing. Both generation and parsing programs 
are executed under the same (chart-based) evaluation strategy.
This results in an efficient, bidirectional (parsing/generation) 
system for Natural Language Processing. Moreover, the system 
possesses ample debugging features, and thus can serve as a 
user-friendly environment for bidirectional grammar design and 
development.