Predicting the Quality of Revisions in Argumentative Writing
Zhexiong Liu Diane Litman Elaine Wang Lindsay Matsumura Richard Correnti
The ability to revise in response to feedback is critical to students'
writing success. In the case of argument writing in specific, identifying
whether an argument revision (AR) is successful or not is a complex problem
because AR quality is dependent on the overall content of an argument. For
example, adding the same evidence sentence could strengthen or weaken existing
claims in different argument contexts (ACs). To address this issue we developed
Chain-of-Thought prompts to facilitate ChatGPT-generated ACs for AR quality
predictions. The experiments on two corpora, our annotated elementary essays
and existing college essays benchmark, demonstrate the superiority of the
proposed ACs over baselines.