@inproceedings{bae-etal-2019-snu,
    title = "{SNU} {IDS} at {S}em{E}val-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational Classification",
    author = "Bae, Sanghwan  and
      Choi, Jihun  and
      Lee, Sang-goo",
    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/iwcs-25-ingestion/S19-2054/",
    doi = "10.18653/v1/S19-2054",
    pages = "312--317",
    abstract = "We present several techniques to tackle the mismatch in class distributions between training and test data in the Contextual Emotion Detection task of SemEval 2019, by extending the existing methods for class imbalance problem. Reducing the distance between the distribution of prediction and ground truth, they consistently show positive effects on the performance. Also we propose a novel neural architecture which utilizes representation of overall context as well as of each utterance. The combination of the methods and the models achieved micro F1 score of about 0.766 on the final evaluation."
}