@inproceedings{ghosh-veale-2018-ironymagnet,
    title = "{I}rony{M}agnet at {S}em{E}val-2018 Task 3: A {S}iamese network for Irony detection in Social media",
    author = "Ghosh, Aniruddha  and
      Veale, Tony",
    editor = "Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      May, Jonathan  and
      Shutova, Ekaterina  and
      Bethard, Steven  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/S18-1093/",
    doi = "10.18653/v1/S18-1093",
    pages = "570--575",
    abstract = "This paper describes our system, entitled IronyMagnet, for the 3rd Task of the SemEval 2018 workshop, {\textquotedblleft}Irony Detection in English Tweets{\textquotedblright}. 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; {\textquotedblleft}Irony by contrast{\textquotedblright} - ironic instances where evaluative expression portrays inverse polarity (positive, negative) of the literal proposition; {\textquotedblleft}Situational irony{\textquotedblright} - ironic instances where output of a situation do not comply with its expectation; {\textquotedblleft}Other verbal irony{\textquotedblright} - 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."
}