@inproceedings{li-xing-2019-clp,
    title = "{CLP} at {S}em{E}val-2019 Task 3: Multi-Encoder in Hierarchical Attention Networks for Contextual Emotion Detection",
    author = "Li, Changjie  and
      Xing, Yun",
    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/ingest-emnlp/S19-2025/",
    doi = "10.18653/v1/S19-2025",
    pages = "164--168",
    abstract = "In this paper, we describe the participation of team ``CLP'' in SemEval-2019 Task 3 ``Con- textual Emotion Detection in Text'' that aims to classify emotion of user utterance in tex- tual conversation. The submitted system is a deep learning architecture based on Hier- archical Attention Networks (HAN) and Em- bedding from Language Model (ELMo). The core of the architecture contains two represen- tation layers. The first one combines the out- puts of ELMo, hand-craft features and Bidi- rectional Long Short-Term Memory with At- tention (Bi-LSTM-Attention) to represent user utterance. The second layer use a Bi-LSTM- Attention encoder to represent the conversa- tion. Our system achieved F1 score of 0.7524 which outperformed the baseline model of the organizers by 0.1656."
}