Evaluating Natural Language Generation (NLG) systems is a notoriously hard problem: Unlike natural language interpretation, where annotated corpora may provide a gold standard against which a system can be measured, there are generally multiple equally good outputs that an NLG system might produce. On the other hand, access to human experimental subjects who could judge the quality of the system's output is usually too expensive for large-scale use. Nevertheless, there has recently been an increased interest in shared tasks and new methodologies for evaluating and comparing NLG systems.

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