SBU Figures It Out: Models Explain Figurative Language

Abstract

Figurative language is ubiquitous in human communication. However, current NLP models are unable to demonstrate a significant understanding of instances of this phenomena. FigLang shared task on figurative language understanding posed the problem of predicting and explaining the relation between a premise and a hypothesis containing an instance of the use of figurative language. We experiment with different variations of using T5-large for this task and build a model that significantly outperforms the task baseline. Treating it as a new task for T5 and simply finetuning on the data achieves the best score on the defined evaluation. Furthermore, we find that hypothesis-only models are able to achieve most of the performance.