Research Output

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.

  • Date:

    01 June 2014

  • Publication Status:


  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    004 Data processing & computer science

  • Funders:

    EU Framework Programme 7 and FP6 and earlier programmes


Gkatzia, D., Hastie, H., & Lemon, O. (2014). Multi-adaptive Natural Language Generation using Principal Component Regression. In Proceedings of the 8th International Natural Language Generation Conference, 138-142



FeedbackGen, Natural Language Generation, Principal Component Regression (PCR),

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