  Automated metaphor detection is a challenging task to identify metaphorical
expressions of words in a sentence. To tackle this problem, we adopt
pre-trained contextualized models, e.g., BERT and RoBERTa. To this end, we
propose a novel metaphor detection model, namely metaphor-aware late
interaction over BERT (MelBERT). Our model not only leverages contextualized
word representation but also benefits from linguistic metaphor identification
theories to distinguish between the contextual and literal meaning of words.
Our empirical results demonstrate that MelBERT outperforms several strong
baselines on four benchmark datasets, i.e., VUA-18, VUA-20, MOH-X, and TroFi.
