@inproceedings{peng-etal-2018-ynu,
    title = "{YNU}-{HPCC} at {S}em{E}val-2018 Task 3: Ensemble Neural Network Models for Irony Detection on {T}witter",
    author = "Peng, Bo  and
      Wang, Jin  and
      Zhang, Xuejie",
    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/ingest_wac_2008/S18-1101/",
    doi = "10.18653/v1/S18-1101",
    pages = "622--627",
    abstract = "This paper describe the system we proposed to participate the first year of Irony detection in English tweets competition. Previous works demonstrate that LSTMs models have achieved remarkable performance in natural language processing; besides, combining multiple classification from various individual classifiers in general is more powerful than a single classification. In order to obtain more precision classification of irony detection, our system trained several individual neural network classifiers and combined their results according to the ensemble-learning algorithm."
}