@InProceedings{gee-wang:2018:S18-1,
  author    = {Gee, Grace  and  Wang, Eugene},
  title     = {psyML at SemEval-2018 Task 1: Transfer Learning for Sentiment and Emotion Analysis},
  booktitle = {Proceedings of The 12th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {369--376},
  abstract  = {In this paper, we describe the first attempt to perform transfer learning from sentiment to emotions. Our system employs Long Short-Term Memory (LSTM) networks, including bidirectional LSTM (biLSTM) and LSTM with attention mechanism. We perform transfer learning by first pre-training the LSTM networks on sentiment data before concatenating the penultimate layers of these networks into a single vector as input to new dense layers. For the E-c subtask, we utilize a novel approach to train models for correlated emotion classes. Our system performs 4/48, 3/39, 8/38, 4/37, 4/35 on all English subtasks EI-reg, EI-oc, V-reg, V-oc, E-c of SemEval 2018 Task 1: Affect in Tweets.},
  url       = {http://www.aclweb.org/anthology/S18-1056}
}