Abstract
End-to-end training with Deep Neural Networks (DNN) is a currently popular method
for metaphor identification. However, standard sequence tagging models do not explicitly take advantage of linguistic theories
of metaphor identification. We experiment
with two DNN models which are inspired
by two human metaphor identification procedures. By testing on three public datasets, we
find that our models achieve state-of-the-art
performance in end-to-end metaphor identification.