@InProceedings{husseiniorabi-EtAl:2018:S18-1,
  author    = {Husseini Orabi, Ahmed  and  Husseini Orabi, Mahmoud  and  Inkpen, Diana  and  Van Bruwaene, David},
  title     = {uOttawa at SemEval-2018 Task 1: Self-Attentive Hybrid GRU-Based Network},
  booktitle = {Proceedings of The 12th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {181--185},
  abstract  = {We propose a novel attentive hybrid GRU-based network (SAHGN), which we used at SemEval-2018 Task 1: Affect in Tweets. Our network has two main characteristics, 1) has the ability to internally optimize its feature representation using attention mechanisms, and 2) provides a hybrid representation using a character level Convolutional Neural Network (CNN), as well as a self-attentive word-level encoder. The key advantage of our model is its ability to signify the relevant and important information that enables self-optimization. Results are reported on the valence intensity regression task.},
  url       = {http://www.aclweb.org/anthology/S18-1027}
}