MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories

TL;DR

MelBERT is a novel metaphor detection model that combines contextualized language models with linguistic theories, outperforming existing methods on multiple benchmark datasets.

Contribution

Introduces MelBERT, a metaphor detection approach that integrates BERT-based models with metaphor identification theories for improved accuracy.

Findings

MelBERT outperforms strong baselines on four benchmark datasets.

The model effectively distinguishes between literal and metaphorical meanings.

Empirical results validate the effectiveness of combining linguistic theories with contextual models.

Abstract

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.