  This paper presents and benchmarks a number of end-to-end Deep Learning based
models for metaphor detection in Greek. We combine Convolutional Neural
Networks and Recurrent Neural Networks with representation learning to bear on
the metaphor detection problem for the Greek language. The models presented
achieve exceptional accuracy scores, significantly improving the previous state
of the art results, which had already achieved accuracy 0.82. Furthermore, no
special preprocessing, feature engineering or linguistic knowledge is used in
this work. The methods presented achieve accuracy of 0.92 and F-score 0.92 with
Convolutional Neural Networks (CNNs) and bidirectional Long Short Term Memory
networks (LSTMs). Comparable results of 0.91 accuracy and 0.91 F-score are also
achieved with bidirectional Gated Recurrent Units (GRUs) and Convolutional
Recurrent Neural Nets (CRNNs). The models are trained and evaluated only on the
basis of the training tuples, the sentences and their labels. The outcome is a
state of the art collection of metaphor detection models, trained on limited
labelled resources, which can be extended to other languages and similar tasks.
