  The ubiquity of metaphor in our everyday communication makes it an important
problem for natural language understanding. Yet, the majority of metaphor
processing systems to date rely on hand-engineered features and there is still
no consensus in the field as to which features are optimal for this task. In
this paper, we present the first deep learning architecture designed to capture
metaphorical composition. Our results demonstrate that it outperforms the
existing approaches in the metaphor identification task.
