@InProceedings{ghosh-veale:2018:S18-1,
  author    = {Ghosh, Aniruddha  and  Veale, Tony},
  title     = {IronyMagnet at SemEval-2018 Task 3: A Siamese network for Irony detection in Social media},
  booktitle = {Proceedings of The 12th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {570--575},
  abstract  = {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 polar- ity (positive, negative) of the literal propo- sition; “Situational irony” - ironic instances where output of a situation do not comply with its expectation; “Other verbal irony” - in- stances 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 subnet- works, each containing a long short term mem- ory layer(LSTM) and an embedding layer ini- tialized with vectors from Glove word embed- ding 1 . The system achieved a f-score of 0.72, and 0.50 in task 1, and task 2 respectively.},
  url       = {http://www.aclweb.org/anthology/S18-1093}
}