Referring-expression generation using a transformation-based learning approach

Abstract

A natural language generation system must generate expressions that allow a reader to identify the entities to which they refer. This paper describes the creation of referring-expression (RE) generation models developed using a transformation-based learning approach. We present an evaluation of the learned models and compare their performance to the performance of a baseline system, which always generates full noun phrase REs. When compared to the baseline system, the learned models produce REs that lead to more coherent natural language documents and are more accurate and closer in length to those that people use.