@inproceedings{enzo-etal-2022-speech,
    title = "Speech acts and Communicative Intentions for Urgency Detection",
    author = "Laurenti, Enzo  and
      Bourgon, Nils  and
      Benamara, Farah  and
      Mari, Alda  and
      Moriceau, V{\'e}ronique  and
      Courgeon, Camille",
    editor = "Nastase, Vivi  and
      Pavlick, Ellie  and
      Pilehvar, Mohammad Taher  and
      Camacho-Collados, Jose  and
      Raganato, Alessandro",
    booktitle = "Proceedings of the 11th Joint Conference on Lexical and Computational Semantics",
    month = jul,
    year = "2022",
    address = "Seattle, Washington",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/dashboard/2022.starsem-1.25/",
    doi = "10.18653/v1/2022.starsem-1.25",
    pages = "289--298",
    abstract = "Recognizing speech acts (SA) is crucial for capturing meaning beyond what is said, making communicative intentions particularly relevant to identify urgent messages. This paper attempts to measure for the first time the impact of SA on urgency detection during crises,006in tweets. We propose a new dataset annotated for both urgency and SA, and develop several deep learning architectures to inject SA into urgency detection while ensuring models generalisability. Our results show that taking speech acts into account in tweet analysis improves information type detection in an out-of-type configuration where models are evaluated in unseen event types during training. These results are encouraging and constitute a first step towards SA-aware disaster management in social media."
}