@InProceedings{vu-EtAl:2018:S18-1,
  author    = {Vu, Thanh  and  Nguyen, Dat Quoc  and  Vu, Xuan-Son  and  Nguyen, Dai Quoc  and  Catt, Michael  and  Trenell, Michael},
  title     = {NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in Twitter},
  booktitle = {Proceedings of The 12th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {525--530},
  abstract  = {This paper describes our NIHRIO system for SemEval-2018 Task 3 “Irony detection in English tweets.” We propose to use a simple neural network architecture of Multilayer Perceptron with various types of input features including: lexical, syntactic, semantic and polarity features. Our system achieves very high performance in both subtasks of binary and multi-class irony detection in tweets. In particular, we rank at least fourth using the accuracy metric and sixth using the F1 metric. Our code is available at: https://github.com/NIHRIO/IronyDetectionInTwitter},
  url       = {http://www.aclweb.org/anthology/S18-1085}
}