  This paper presents a machine learning approach to discourse planning in
natural language generation. More specifically, we address the problem of
learning the most natural ordering of facts in discourse plans for a specific
domain. We discuss our methodology and how it was instantiated using two
different machine learning algorithms. A quantitative evaluation performed in
the domain of museum exhibit descriptions indicates that our approach performs
significantly better than manually constructed ordering rules. Being
retrainable, the resulting planners can be ported easily to other similar
domains, without requiring language technology expertise.
