  Identifying metaphors in text is very challenging and requires comprehending
the underlying comparison. The automation of this cognitive process has gained
wide attention lately. However, the majority of existing approaches concentrate
on word-level identification by treating the task as either single-word
classification or sequential labelling without explicitly modelling the
interaction between the metaphor components. On the other hand, while existing
relation-level approaches implicitly model this interaction, they ignore the
context where the metaphor occurs. In this work, we address these limitations
by introducing a novel architecture for identifying relation-level metaphoric
expressions of certain grammatical relations based on contextual modulation. In
a methodology inspired by works in visual reasoning, our approach is based on
conditioning the neural network computation on the deep contextualised features
of the candidate expressions using feature-wise linear modulation. We
demonstrate that the proposed architecture achieves state-of-the-art results on
benchmark datasets. The proposed methodology is generic and could be applied to
other textual classification problems that benefit from contextual interaction.
