Deep Learning based, end-to-end metaphor detection in Greek language with Recurrent and Convolutional Neural Networks

TL;DR

This paper introduces end-to-end deep learning models combining CNNs and RNNs for metaphor detection in Greek, achieving state-of-the-art accuracy without linguistic features or preprocessing.

Contribution

It presents novel deep learning architectures for metaphor detection in Greek that outperform previous methods and require minimal linguistic resources.

Findings

Achieved 0.92 accuracy and F-score with CNNs and LSTMs.

Attained 0.91 accuracy and F-score with GRUs and CRNNs.

Models trained solely on labeled sentence data without feature engineering.

Abstract

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.