Is It That Difficult to Find a Good Preference Order for the Incremental Algorithm?
Article first published online: 1 JUN 2012
Copyright © 2012 Cognitive Science Society, Inc.
Volume 36, Issue 5, pages 837–841, July 2012
How to Cite
Krahmer, E., Koolen, R. and Theune, M. (2012), Is It That Difficult to Find a Good Preference Order for the Incremental Algorithm?. Cognitive Science, 36: 837–841. doi: 10.1111/j.1551-6709.2012.01258.x
- Issue published online: 3 JUL 2012
- Article first published online: 1 JUN 2012
- Received 1 November 2011; received in revised form 13 February 2012; accepted 15 February 2012
- Generation/production of referring expressions;
- Evaluation metrics for generation algorithms;
- Incremental algorithm;
- Learning curve experiments
In a recent article published in this journal (van Deemter, Gatt, van der Sluis, & Power, 2012), the authors criticize the Incremental Algorithm (a well-known algorithm for the generation of referring expressions due to Dale & Reiter, 1995, also in this journal) because of its strong reliance on a pre-determined, domain-dependent Preference Order. The authors argue that there are potentially many different Preference Orders that could be considered, while often no evidence is available to determine which is a good one. In this brief note, however, we suggest (based on a learning curve experiment) that finding a Preference Order for a new domain may not be so difficult after all, as long as one has access to a handful of human-produced descriptions collected in a semantically transparent way. We argue that this is due to the fact that it is both more important and less difficult to get a good ordering of the head than of the tail of a Preference Order.