@inproceedings{dong-etal-2020-transformer,
    title = "Transformer-based Context-aware Sarcasm Detection in Conversation Threads from Social Media",
    author = "Dong, Xiangjue  and
      Li, Changmao  and
      Choi, Jinho D.",
    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://preview.aclanthology.org/add-emnlp-2024-awards/2020.figlang-1.38/",
    doi = "10.18653/v1/2020.figlang-1.38",
    pages = "276--280",
    abstract = "We present a transformer-based sarcasm detection model that accounts for the context from the entire conversation thread for more robust predictions. Our model uses deep transformer layers to perform multi-head attentions among the target utterance and the relevant context in the thread. The context-aware models are evaluated on two datasets from social media, Twitter and Reddit, and show 3.1{\%} and 7.0{\%} improvements over their baselines. Our best models give the F1-scores of 79.0{\%} and 75.0{\%} for the Twitter and Reddit datasets respectively, becoming one of the highest performing systems among 36 participants in this shared task."
}