Paper: Does Size Matter – How Much Data is Required to Train a REG Algorithm?

ACL ID P11-2116
Title Does Size Matter – How Much Data is Required to Train a REG Algorithm?
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

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 be- tween different-sized training sets, a system- atic training method was developed. The re- sults show that depending on the complexity of the domain, training on 10 to 20 items may already lead to a good performance.