@InProceedings{nyegaardsignori-EtAl:2018:S18-1,
  author    = {Nyegaard-Signori, Thomas  and  Helms, Casper Veistrup  and  Bjerva, Johannes  and  Augenstein, Isabelle},
  title     = {KU-MTL at SemEval-2018 Task 1: Multi-task Identification of Affect in Tweets},
  booktitle = {Proceedings of The 12th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {385--389},
  abstract  = {We take a multi-task learning approach to the shared Task 1 at SemEval-2018. The general idea concerning the model structure is to use as little external data as possible in order to preserve the task relatedness and reduce complexity. We employ multi-task learning with hard parameter sharing to exploit the relatedness between sub-tasks. As a base model, we use a standard recurrent neural network for both the classification and regression subtasks. Our system ranks 32nd out of 48 participants with a Pearson score of 0.557 in the first subtask, and 20th out of 35 in the fifth subtask with an accuracy score of 0.464.},
  url       = {http://www.aclweb.org/anthology/S18-1058}
}