论文标题
及时的不可知论散文得分手：一种域概括的方法，用于交叉宣传的自动化论文评分
Prompt Agnostic Essay Scorer: A Domain Generalization Approach to Cross-prompt Automated Essay Scoring
论文作者
论文摘要
交叉宣传的自动论文评分（AES）要求系统使用非目标推出论文来为目标推出论文授予分数。由于获得了特定提示的大量预先毕业的论文通常是困难且不现实的，因此交叉宣传的任务对于开发现实世界中的AES系统至关重要，但它仍然是研究不足的研究领域。专为迅速特异性AE设计而设计的模型在很大程度上依赖于特定的知识，并且在交叉点环境中表现不佳，而当前的交叉竞争方法AES的方法要么需要一定数量的标签目标贡献论文，要么需要大量的无标记的目标派遣论文来以多步骤的方式进行转移学习。为了解决这些问题，我们介绍了迅速的不可知论论文得分手（PAE）以进行交叉推荐。我们的方法不需要在培训期间访问标记或未标记的目标prompt数据，并且是一种单级方法。 PAE在实践中很容易应用，并在自动化学生评估奖（ASAP）数据集上实现最先进的表现。
Cross-prompt automated essay scoring (AES) requires the system to use non target-prompt essays to award scores to a target-prompt essay. Since obtaining a large quantity of pre-graded essays to a particular prompt is often difficult and unrealistic, the task of cross-prompt AES is vital for the development of real-world AES systems, yet it remains an under-explored area of research. Models designed for prompt-specific AES rely heavily on prompt-specific knowledge and perform poorly in the cross-prompt setting, whereas current approaches to cross-prompt AES either require a certain quantity of labelled target-prompt essays or require a large quantity of unlabelled target-prompt essays to perform transfer learning in a multi-step manner. To address these issues, we introduce Prompt Agnostic Essay Scorer (PAES) for cross-prompt AES. Our method requires no access to labelled or unlabelled target-prompt data during training and is a single-stage approach. PAES is easy to apply in practice and achieves state-of-the-art performance on the Automated Student Assessment Prize (ASAP) dataset.
