Bigrams and BiLSTMs Two Neural Networks for Sequential Metaphor Detection

We present and compare two alternative deep neural architectures to perform word-level metaphor detection on text: a bi-LSTM model and a new structure based on recursive feedforward concatenation of the input. We discuss different versions of such models and the effect that input manipulation -specifically, reducing the length of sentences and introducing concreteness scores for words -have on their performance.