@inproceedings{lee-etal-2020-augmenting,
    title = "Augmenting Data for Sarcasm Detection with Unlabeled Conversation Context",
    author = "Lee, Hankyol  and
      Yu, Youngjae  and
      Kim, Gunhee",
    editor = "Klebanov, Beata Beigman  and
      Shutova, Ekaterina  and
      Lichtenstein, Patricia  and
      Muresan, Smaranda  and
      Wee, Chee  and
      Feldman, Anna  and
      Ghosh, Debanjan",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.figlang-1.2",
    doi = "10.18653/v1/2020.figlang-1.2",
    pages = "12--17",
    abstract = "We present a novel data augmentation technique, CRA (Contextual Response Augmentation), which utilizes conversational context to generate meaningful samples for training. We also mitigate the issues regarding unbalanced context lengths by changing the input output format of the model such that it can deal with varying context lengths effectively. Specifically, our proposed model, trained with the proposed data augmentation technique, participated in the sarcasm detection task of FigLang2020, have won and achieves the best performance in both Reddit and Twitter datasets.",
}