Simulating Sense Extension

Abstract Humans have an uncanny ability to push words beyond their limits. This ability manifests in many phenomena, including metaphor, metonymy, semantic drift, slang, jargon, conversion, and overextension. Generative models of these phenomena are uncommon, because there is no robust methodology that can be used to train and evaluate models of this nature. To address this, we introduce a new task, novel sense formulation, in which a model is exposed to a multimodal representation of an unseen concept and must use an existing word creatively to describe it. We create seven datasets corresponding to the phenomena above, and evaluate a perceptron, a transformer, and an influential cognitive model. For most phenomena, a multimodal variant of the perceptron performed best. The cognitive model underperformed, suggesting that its description of the mechanism behind sense extension is incomplete.