@InProceedings{ili-EtAl:2018:WASSA2018,
  author    = {Ilić, Suzana  and  Marrese-Taylor, Edison  and  Balazs, Jorge  and  Matsuo, Yutaka},
  title     = {Deep contextualized word representations for detecting sarcasm and irony},
  booktitle = {Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis},
  month     = {October},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  pages     = {2--7},
  abstract  = {Predicting context-dependent and non-literal utterances like sarcastic and ironic expressions still remains a challenging task in NLP, as it goes beyond linguistic patterns, encompassing common sense and shared knowledge as crucial components. To capture complex morpho-syntactic features that can usually serve as indicators for irony or sarcasm across dynamic contexts, we propose a model that uses character-level vector representations of words, based on ELMo. We test our model on 7 different datasets derived from 3 different data sources, providing state-of-the-art performance in 6 of them, and otherwise offering competitive results.},
  url       = {http://aclweb.org/anthology/W18-6202}
}