@inproceedings{kim-etal-2018-attnconvnet,
    title = "{A}ttn{C}onvnet at {S}em{E}val-2018 Task 1: Attention-based Convolutional Neural Networks for Multi-label Emotion Classification",
    author = "Kim, Yanghoon  and
      Lee, Hwanhee  and
      Jung, Kyomin",
    editor = "Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      May, Jonathan  and
      Shutova, Ekaterina  and
      Bethard, Steven  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/S18-1019/",
    doi = "10.18653/v1/S18-1019",
    pages = "141--145",
    abstract = "In this paper, we propose an attention-based classifier that predicts multiple emotions of a given sentence. Our model imitates human`s two-step procedure of sentence understanding and it can effectively represent and classify sentences. With emoji-to-meaning preprocessing and extra lexicon utilization, we further improve the model performance. We train and evaluate our model with data provided by SemEval-2018 task 1-5, each sentence of which has several labels among 11 given emotions. Our model achieves 5th/1st rank in English/Spanish respectively."
}