  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.
