Expressions used to refer to entities in a common environment do not originate solely from one participant in a dialogue but are formed collaboratively. It is possible to train a model for resolving these referring expressions (REs) in a static manner using an appropriate corpus, but, due to the collaborative nature of their formation, REs are highly dependent not only on attributes of the referent in question (e.g. color, shape) but also on the dialogue participants themselves. As a proof of concept, we improved the accuracy of a words-as-classifiers logistic regression  model  by  incorporating  knowledge about  accepting/rejecting REs proposed from other participants.