Natural Language Generation in an MT System Based on a Multi-Level Processing Strategy
Huifeng Li, Sin-Jae Kang, Jong-Hyeok Lee · International Journal of Computer Processing Of Languages · 2001
This paper describes how to generate high quality Korean sentences from intermediate meaning representations. In previous research, there is no clear-cut separation between syntactic and morphological processing, and lexical information and rules are so tightly combined. This creates difficulties, such as attempting to enhance portability and extensibility, or handling complex linguistic phenomena in a systematic manner. We adopted Mel'čuk's meaning-text model as a linguistic model in Korean generation part, in which the complicated task of Korean generation can be broken up into logically independent subtasks, making the system highly modularized and robust. In a Korean generation experiment, the system showed a fidelity rate of 90% and an intelligibility rate of 85%, which is a promising result considering the difficulties with generating various linguistic phenomena.