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 language | English |
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Title of host publication | Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics (ACL) |
Publisher | Association for Computational Linguistics |
Pages | 1231-1240 |
Number of pages | 10 |
Volume | 1 |
ISBN (Print) | 9781937284725 |
DOIs | |
Publication status | Published - 22 Jun 2014 |
Event | 52nd Annual Meeting of the Association for Computational Linguistics - Baltimore, United States Duration: 22 Jun 2014 → 27 Jun 2014 http://acl2014.org/home.htm |
Conference
Conference | 52nd Annual Meeting of the Association for Computational Linguistics |
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Abbreviated title | ACL 2014 |
Country/Territory | United States |
City | Baltimore |
Period | 22/06/14 → 27/06/14 |
Internet address |