Comparing multi-label classification with reinforcement learning for summarisation of time-series data

Dimitra Gkatzia, Helen Hastie, Oliver Lemon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of 52nd Annual Meeting of the Association for Computational Linguistics (ACL)
PublisherAssociation for Computational Linguistics
Pages1231-1240
Number of pages10
Volume1
ISBN (Print)9781937284725
Publication statusPublished - 2014
Event52nd Annual Meeting of the Association for Computational Linguistics - Baltimore, MD, United Kingdom
Duration: 22 Jun 201427 Jun 2014

Conference

Conference52nd Annual Meeting of the Association for Computational Linguistics
Abbreviated titleACL 2014
CountryUnited Kingdom
CityBaltimore, MD
Period22/06/1427/06/14

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

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  • Cite this

    Gkatzia, D., Hastie, H., & Lemon, O. (2014). Comparing multi-label classification with reinforcement learning for summarisation of time-series data. In Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics (ACL) (Vol. 1, pp. 1231-1240). Association for Computational Linguistics.