Papers › End-to-End Sequential Metaphor Identification Inspired by Linguistic Theories
End-to-End Sequential Metaphor Identification Inspired by Linguistic Theories
Rui Mao, Chenghua Lin, Frank Guerin
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.
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