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.
['Mariët Theune', 'Ruud Koolen', 'Emiel Krahmer', 'Sander Wubben']
Does Size Matter -- How Much Data is Required to Train a REG Algorithm?