Abstract. Metaphors are very intriguing elements of human language that are surprisingly prevalent in our everyday communications. In fact, studies show that the human brain processes conventional metaphors in the same speed as literal language. Nevertheless, the computational linguistics literature consistently treats metaphors as a separate domain to literal language. This study investigates the potential of constructing systems that can jointly handle metaphoric and literal sentences by leveraging the newfound capabilities of deep learning systems. We narrow the scope of the report, following earlier work, to evaluate deep learning systems fine-tuned on the task of textual entailment (TE). We argue that TE is a task naturally suited to the interpretation of metaphoric language. We show that TE systems can improve significantly in metaphoric performance by being fine tuned on a small dataset with metaphoric premises. Even though the improvement in performance on metaphors is typically accompanied by a drop in performance on the original dataset we note that auto-regressive models seem to show a smaller drop in performance on literal examples compared to other types of models.
Recommended citation: Artemis Panagopoulou, Mitch Marcus (2020). "Metaphor and Entailment: Looking at metaphors through the lens of textual entailment." Not Published.