@InProceedings{gao-chen:2018:S18-1,
  author    = {Gao, Zi Yuan  and  Chen, Chia-Ping},
  title     = {deepSA2018 at SemEval-2018 Task 1: Multi-task Learning of Different Label for 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     = {226--230},
  abstract  = {This paper describes our system implementation for subtask V-oc of SemEval-2018 Task 1: affect in tweets. We use multi-task learning method to learn shared representation, then learn the features for each task. There are five classification models in the proposed multi-task learning approach. These classification models are trained sequentially to learn different features for different classification tasks. In addition to the data released for SemEval-2018, we use datasets from previous SemEvals during system construction. Our Pearson correlation score is 0.638 on the official SemEval-2018 Task 1 test set.},
  url       = {http://www.aclweb.org/anthology/S18-1034}
}