IronyMagnet at SemEval-2018 Task 3: A Siamese network for Irony detection in Social media

This paper describes our system, entitled IronyMagnet, for the 3rd Task of the SemEval 2018 workshop, {``}Irony Detection in English Tweets{''}. In Task 1, irony classification task has been considered as a binary classification task. Now for the first time, finer categories of irony are considered as part of a shared task. In task 2, three types of irony are considered; {``}Irony by contrast{''} - ironic instances where evaluative expression portrays inverse polarity (positive, negative) of the literal proposition; {``}Situational irony{''} - ironic instances where output of a situation do not comply with its expectation; {``}Other verbal irony{''} - instances where ironic intent does not rely on polarity contrast or unexpected outcome. We proposed a Siamese neural network for irony detection, which is consisted of two subnetworks, each containing a long short term memory layer(LSTM) and an embedding layer initialized with vectors from Glove word embedding 1 . The system achieved a f-score of 0.72, and 0.50 in task 1, and task 2 respectively.