@InProceedings{basile-EtAl:2019:S19-22,
  author    = {Basile, Angelo  and  Franco-Salvador, Marc  and  Pawar, Neha  and  Štajner, Sanja  and  Chinea Rios, Mara  and  Benajiba, Yassine},
  title     = {SymantoResearch at SemEval-2019 Task 3: Combined Neural Models for Emotion Classification in Human-Chatbot Conversations},
  booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {330--334},
  abstract  = {In this paper, we present our participation to the EmoContext shared task on detecting emotions in English textual conversations between a human and a chatbot. We propose four neural systems and combine them to further improve the results. We show that our neural ensemble systems can successfully distinguish three emotions (SAD, HAPPY, and ANGRY) and separate them from the rest (OTHERS) in a highly-imbalanced scenario. Our best system achieved a 0.77 F1-score and was ranked fourth out of 165 submissions.},
  url       = {http://www.aclweb.org/anthology/S19-2057}
}