@inproceedings{ma-etal-2019-pkuse,
    title = "{PKUSE} at {S}em{E}val-2019 Task 3: Emotion Detection with Emotion-Oriented Neural Attention Network",
    author = "Ma, Luyao  and
      Zhang, Long  and
      Ye, Wei  and
      Hu, Wenhui",
    editor = "May, Jonathan  and
      Shutova, Ekaterina  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/S19-2049/",
    doi = "10.18653/v1/S19-2049",
    pages = "287--291",
    abstract = "This paper presents the system in SemEval-2019 Task 3, {\textquotedblleft}EmoContext: Contextual Emotion Detection in Text{\textquotedblright}. We propose a deep learning architecture with bidirectional LSTM networks, augmented with an emotion-oriented attention network that is capable of extracting emotion information from an utterance. Experimental results show that our model outperforms its variants and the baseline. Overall, this system has achieved 75.57{\%} for the microaveraged F1 score."
}