Is It That Difficult to Find a Good Preference Order for the Incremental Algorithm?

Cognitive Science 36 (5):837-841 (2012)
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.
Keywords Learning curve experiments  Generation/production of referring expressions  Incremental algorithm  Reference  Evaluation metrics for generation algorithms  Psycholinguistics
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DOI 10.1111/j.1551-6709.2012.01258.x
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