In the sentence, “Every pedestrian held an umbrella”, most English-speakers would assume that there was one umbrella per pedestrian, even though the syntax allows for there to be a single umbrella held by every pedestrian at once. On the other hand, “Every pilgrim visited a shrine” allows for a plausible interpretation that all the pilgrims visited a single shrine. Some combination of experience and common sense allows language users to assign relative probable set cardinalities to different pairs of objects: e.g. “pedestrian ~ umbrella” while “pilgrim > shrine”.
This project will attempt to use deep learning techniques over textual and image data sources as well as data collected from human experiments in order to build a preliminary general model of relative set cardinality assignment, in the context of universal quantifiers like “every”.