标题风格迁移中自动评价与任务框架的交互作用
On the interaction of automatic evaluation and task framing in headline style transfer
摘要：
NLG社区正在进行的一场辩论涉及评估系统的最佳方式，与基于语料库的指标相比，人类评估通常被认为是最可靠的方法。然而，涉及微妙的文本差异的任务，如风格转换，往往是人类难以执行的。在本文中，我们提出了一种评估方法，这项任务的基础上经过严格训练的分类器，它比传统的指标，如BLEU和ROUGE更好地反映系统的差异。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
