感知分数，一个开放式文本生成评估的学习指标
Perception Score, A Learned Metric for Open-ended Text Generation Evaluation
摘要：
开放式自然语言生成任务的自动评估仍然是一个挑战。BLEU等现有指标与人类判断的相关性较低。我们提出了一种新颖而强大的基于学习的评估指标：感知分数。该方法从整体上衡量生成的整体质量并进行评分，而不是只关注一个评估标准，例如单词重叠。此外，它还显示了其评估结果的不确定性。感知评分通过将不确定性联系起来，对发电系统给出了更准确的评价。Perception Score提供了两个条件生成任务和两个无条件生成任务的最新结果。
英文摘要：
Automatic evaluation for open-ended natural language generation tasks remains a challenge. Existing metrics such as BLEU show a low correlation with human judgment. We propose a novel and powerful learning-based evaluation metric: Perception Score. The method measures the overall quality of the generation and scores holistically instead of only focusing on one evaluation criteria, such as word overlapping. Moreover, it also shows the amount of uncertainty about its evaluation result. By connecting the uncertainty, Perception Score gives a more accurate evaluation for the generation system. Perception Score provides state-of-the-art results on two conditional generation tasks and two unconditional generation tasks.
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。
