BiLSTM-based models for metaphor detection

Abstract

Metaphor is a pervasive phenomenon in our daily use of natural language. Metaphor detection has been playing an important role in a variety of NLP tasks. Most existing approaches to this task rely heavily on the use of human-crafted features built from linguistic knowledge resource, which greatly limits their applicability. This paper presents four BiLSTM-based models for metaphor detection. The first three models use a sub-sequence as the input to BiLSTM network, each with a special kind of sub-sequence extracted from the input sentence. The last model is an ensemble model which aggregate the outputs from the first three models to get the final output. Experimental results have shown the effectiveness of our models.