Multi-adaptive Natural Language Generation using Principal Component Regression

We present FeedbackGen, a system that uses a multi-adaptive approach to Natural Language Generation. With the term ‘multi-adaptive’, we refer to a system that is able to adapt its content to different user groups simultaneously, in our case adapting to both lecturers and students. We present a novel approach to student feedback generation, which simultaneously takes into account the preferences of lecturers and students when determining the content to be conveyed in a feedback summary. In this framework, we utilise knowledge derived from ratings on feedback summaries by extracting the most relevant features using Principal Component Regression (PCR) analysis. We then model a reward function that is used for training a Reinforcement Learning agent. Our results with students suggest that, from the students’ perspective, such an approach can generate more preferable summaries than a purely lecturer-adapted approach.