  Idiomatic expressions are an integral part of natural language and constantly
being added to a language. Owing to their non-compositionality and their
ability to take on a figurative or literal meaning depending on the sentential
context, they have been a classical challenge for NLP systems. To address this
challenge, we study the task of detecting whether a sentence has an idiomatic
expression and localizing it. Prior art for this task had studied specific
classes of idiomatic expressions offering limited views of their
generalizability to new idioms. We propose a multi-stage neural architecture
with the attention flow mechanism for identifying these expressions. The
network effectively fuses contextual and lexical information at different
levels using word and sub-word representations. Empirical evaluations on three
of the largest benchmark datasets with idiomatic expressions of varied
syntactic patterns and degrees of non-compositionality show that our proposed
model achieves new state-of-the-art results. A salient feature of the model is
its ability to identify idioms unseen during training with gains from 1.4% to
30.8% over competitive baselines on the largest dataset.
