Research Output

Comparing Multi-label Classification with Reinforcement Learning for Summarisation of Time-series Data

  We present a novel approach for automatic report generation from time-series data, in the context of student feedback generation. Our proposed methodology treats content selection as a multi-label (ML)
classification problem, which takes as input time-series data and outputs a set of templates, while capturing the dependencies between selected templates. We show that this method generates output closer to the feedback that lecturers actually generated, achieving 3.5% higher accuracy and 15% higher F-score than multiple simple classifiers that keep a history of selected templates. Furthermore, we compare a ML classifier with a Reinforcement Learning (RL) approach in simulation and using ratings from real student users. We show that the different methods have different benefits, with ML being more
accurate for predicting what was seen in the training data, whereas RL is more exploratory and slightly preferred by the students.

  • Date:

    31 December 2014

  • Publication Status:

    Unpublished

  • Publisher

    Association for Computational Linguistics (ACL)

  • DOI:

    10.3115/v1/p14-1116

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    006.3 Artificial intelligence

  • Funders:

    Historic Funder (pre-Worktribe)

Citation

Gkatzia, D., Hastie, H., & Lemon, O. (2014). Comparing Multi-label Classification with Reinforcement Learning for Summarisation of Time-series Data. In Proceedings of the Conference Volume 1: Long Papers, 1231-1240. https://doi.org/10.3115/v1/p14-1116

Authors

Keywords

Reinforcement Learning, RL, multi-label, ML.

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