论文标题
专业知识风格转移：专家与外行之间更好地沟通的新任务
Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen
论文作者
论文摘要
知识的诅咒会阻碍专家与外行之间的沟通。我们提出了一项新的专业知识风格转移任务，并贡献了一个手动注释的数据集，目的是减轻这种认知偏见。解决此任务不仅简化了专业语言，还可以使用简单的单词提高外行描述的准确性和专业知识水平。这是一项具有挑战性的任务，在以前的工作中没有解决，因为它要求模型具有专家智能，以便对文本进行深入了解域知识和结构。我们建立了五个最先进模型的基准性能，用于样式传输和文本简化。结果表明机器和人类性能之间存在显着差距。我们还讨论了自动评估的挑战，以提供对未来研究方向的见解。该数据集可在https://srhthu.github.io/expertise-style-transfer上公开获取。
The curse of knowledge can impede communication between experts and laymen. We propose a new task of expertise style transfer and contribute a manually annotated dataset with the goal of alleviating such cognitive biases. Solving this task not only simplifies the professional language, but also improves the accuracy and expertise level of laymen descriptions using simple words. This is a challenging task, unaddressed in previous work, as it requires the models to have expert intelligence in order to modify text with a deep understanding of domain knowledge and structures. We establish the benchmark performance of five state-of-the-art models for style transfer and text simplification. The results demonstrate a significant gap between machine and human performance. We also discuss the challenges of automatic evaluation, to provide insights into future research directions. The dataset is publicly available at https://srhthu.github.io/expertise-style-transfer.
