Reducing Annotation Efforts in Supervised Short Answer Scoring

Automated short answer scoring is increasingly used to give students timely feedback about their learning progress. Building scoring models comes with high costs, as state-of-the-art methods using supervised learning require large amounts of hand-annotated data. We analyze the potential of recently proposed methods for semi-supervised learning based on clustering. We find that all examined methods (centroids, all clusters, selected pure clusters) are mainly effective for very short answers and do not generalize well to several-sentence responses.

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Conference:
NAACL HLT 2015, The Tenth Workshop on Innovative Use of NLP for Building Educational Applications, June 4, 2015 Denver, Colorado, USA
Date Issued:
2015
URN:
urn:nbn:de:hbz:464-20211022-130923-8
DOI:
10.3115/v1/W15-0615
Language:
English
Type of Resource:
Text
Subject categories of the Deutsche Nationalbibliografie:
004 Data processing Computer sciences
Hosting institution:
Faculty of Engineering, Computer Science and Applied Cognitive Science, Computer Science, Language Technology Lab
Info on primary publication:
Zesch, T., Heilman, M., Cahill, A. (2015). Reducing Annotation Efforts in Supervised Short Answer Scoring. In: Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications, 2015, pp.124–132. Association for Computational Linguistics. https://doi.org/10.3115/v1/W15-0615

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