Capturing acceptable variation in distinguishing descriptions

Almost all existing referring expression generation algorithms aim to find one best referring expression for a given intended referent. However, human-produced data demonstrates that, for any given entity, many perfectly acceptable referring expressions exist. At the same time, it is not the case that all logically possible descriptions are acceptable; so, if we remove the requirement to produce only one best solution, how do we avoid generating undesirable descriptions? Our aim in this paper is to sketch a framework that allows us to capture constraints on referring expression generation, so that the set of logically possible descriptions can be reduced to just those that are acceptable.