Does size matter: how much data is required to train a REG algorithm?

Authors:
Mariët Theune;Ruud Koolen;Emiel Krahmer;Sander Wubben
Affiliations:
University of Twente, AE Enschede, The Netherlands;Tilburg University, LE Tilburg, The Netherlands;Tilburg University, LE Tilburg, The Netherlands;Tilburg University, LE Tilburg, The Netherlands
Venue:
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Year:
2011

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Abstract

In this paper we investigate how much data is required to train an algorithm for attribute selection, a subtask of Referring Expressions Generation (REG). To enable comparison between different-sized training sets, a systematic training method was developed. The results show that depending on the complexity of the domain, training on 10 to 20 items may already lead to a good performance.