  An ongoing debate in the NLG community concerns the best way to evaluate
systems, with human evaluation often being considered the most reliable method,
compared to corpus-based metrics. However, tasks involving subtle textual
differences, such as style transfer, tend to be hard for humans to perform. In
this paper, we propose an evaluation method for this task based on
purposely-trained classifiers, showing that it better reflects system
differences than traditional metrics such as BLEU and ROUGE.
