  Metaphors are ubiquitous in natural language, and their detection plays an
essential role in many natural language processing tasks, such as language
understanding, sentiment analysis, etc. Most existing approaches for metaphor
detection rely on complex, hand-crafted and fine-tuned feature pipelines, which
greatly limit their applicability. In this work, we present an end-to-end
method composed of deep contextualized word embeddings, bidirectional LSTMs and
multi-head attention mechanism to address the task of automatic metaphor
detection. Our method, unlike many other existing approaches, requires only the
raw text sequences as input features to detect the metaphoricity of a phrase.
We compare the performance of our method against the existing baselines on two
benchmark datasets, TroFi, and MOH-X respectively. Experimental evaluations
confirm the effectiveness of our approach.
